System

Abstract

A system is provided.SOLUTION: A system comprising: a sensor module that collects a user's musical instrument performance in real-time; an analysis server that analyzes the data collected from the sensor module and compares it to an expert performance database; and a terminal that visually and audibly presents feedback from the analysis server to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Musicians and music students face the challenge of not receiving immediate feedback during practice, and are unable to receive constant guidance from teachers or professional performers. Furthermore, it is difficult for them to objectively understand the problems with their own performance. This hinders efficient practice and rapid improvement of their skills. [Means for solving the problem]

[0005] To solve this problem, we provide a system that includes a sensor module that collects a user's musical instrument performance in real time, an analysis server that analyzes the data collected from the sensor module and compares it with an expert performance database, and a terminal that presents visual and auditory feedback from the analysis server to the user. The sensor module collects audio data, motion data, and environmental data, and the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data and compares it with the expert performance database to provide real-time feedback on the user's performance technique. This allows the user to quickly improve their performance technique.

[0006] "User" refers to a performer who plays an instrument using this system.

[0007] "Playing an instrument" refers to the act of a user playing music using a particular instrument.

[0008] "Real-time" refers to processing and reactions occurring almost immediately in line with the progression of real time.

[0009] A "sensor module" refers to a device equipped with various sensors for collecting data related to the user's musical instrument performance.

[0010] "Analysis server" refers to a computer that analyzes data sent from the sensor module and performs calculations to generate feedback.

[0011] "Feedback" refers to evaluations and instructions for improvement of the user's performance generated by the analysis server.

[0012] "Terminal" refers to a display device worn or used by a user, which has the function of presenting visual and audible feedback from the analysis server.

[0013] "Audio data" refers to data that records the sound of a musical instrument in digital format.

[0014] "Motion data" refers to data related to the movements of the user's hands and body.

[0015] "Environmental data" refers to data related to the performance environment, such as background sounds and location information.

[0016] "Pitch" is an element that indicates the pitch of a sound, and refers to a sound that corresponds to a specific frequency.

[0017] "Rhythm" refers to the pattern in which sounds occur in succession over time.

[0018] "Positioning" refers to the position and posture of the user's hands and fingers.

[0019] "Finger movements" refers to data that indicates how the user's fingers move while playing.

[0020] An "expert performance database" refers to a database that accumulates performance data from professional performers. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. This system is primarily composed of a user device (AR glasses), a sensor module, an analysis server, and a program that links these devices.

[0043] System Overview

[0044] 1. User device (AR glasses):

[0045] The AR glasses worn by the user have a built-in sensor module that collects performance data in real time.

[0046] When the user starts playing, the terminal sends the collected data to the analysis server.

[0047] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0048] 2. Sensor module:

[0049] The module contains various sensors that collect audio, motion, and environmental data.

[0050] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0051] Environmental sensors collect environmental factors such as background noise and location information.

[0052] 3. Analysis Server:

[0053] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0054] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[0055] Feedback is provided in visual and audio formats to aid user understanding.

[0056] Program processing

[0057] When the user starts playing the instrument, the device collects data through the sensor module, which collects audio data, motion data, and environmental data.

[0058] The terminal transmits the collected data to an analysis server in real time.

[0059] The analysis server analyzes the received data and detects errors in pitch and rhythm, finger movements, etc. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance with that of other users.

[0060] The analysis server generates feedback for the user based on the identified differences, including pitch and rhythm corrections, and correct finger and hand positioning.

[0061] The analytics server sends the generated feedback to the device, which then displays it instantly in the user's field of view, for example by using on-screen arrows or highlights to indicate correct finger placement and by providing specific textual suggestions for improvement.

[0062] Specific examples

[0063] Example 1: Piano practice

[0064] When the user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[0065] The device transmits this data to an analysis server in real time.

[0066] The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[0067] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster."

[0068] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0069] Example 2: Violin practice

[0070] When the user starts playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning).

[0071] The device transmits this data to an analysis server in real time.

[0072] The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch.

[0073] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0074] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0075] The above is a concrete example of the program processing in this system. This system allows users to efficiently improve their playing skills.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The user puts on the AR glasses and starts the system, which launches the user interface and establishes a connection with the sensor module and analysis server.

[0079] Step 2:

[0080] When a user starts playing an instrument, the device's sensor module starts collecting performance data: audio data from a microphone, motion data from an IMU (inertial measurement unit), and environmental data from environmental sensors.

[0081] Step 3:

[0082] The collected performance data is sent from the device to an analysis server in real time, and the data is transferred securely over the network.

[0083] Step 4:

[0084] The analysis server analyzes the received data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and instrument positioning from the motion data, and using environmental data for noise reduction.

[0085] Step 5:

[0086] Based on the analysis results, the analysis server compares the user's performance with a database of expert performances to identify differences between the expert's performance and the user's, such as pitch discrepancies, rhythmic delays, and incorrect hand and finger positioning.

[0087] Step 6:

[0088] Based on the identified discrepancies, the analysis server generates feedback, which can include pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual instructions for specific improvements.

[0089] Step 7:

[0090] The generated feedback is sent from the analysis server to the device, and is provided in visual and auditory forms.

[0091] Step 8:

[0092] The device displays the received feedback in the user's field of view, for example, arrows or highlights to indicate correct finger placement, text descriptions of pitch discrepancies, etc. Audio feedback may also be played.

[0093] Step 9:

[0094] Based on the provided feedback, the user can then modify their playing technique and try again, during which time data is collected, analyzed, and feedback is provided again.

[0095] Step 10:

[0096] By repeating this feedback loop, users can quickly and efficiently improve their playing skills.

[0097] Example 1

[0098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0099] Traditional methods of music education require face-to-face lessons with expert instruction, which are subject to time and geographical constraints. Self-practice also increases the risk of continuing to use incorrect techniques, making it difficult to improve efficiently. Furthermore, the lack of real-time feedback slows down the cycle of performance improvement.

[0100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0101] In this invention, the server includes a sensor module means for collecting the user's performance in real time, an analysis server means for analyzing the data collected from the sensor module means and comparing it with a performance database of experts, and a terminal means for visually and audibly presenting feedback from the analysis server means to the user, thereby enabling the user to receive expert guidance in real time.

[0102] "User" refers to an individual who utilizes the system to play an instrument and receive feedback.

[0103] "Performing" refers to the act of playing music using an instrument.

[0104] "Real-time" refers to processing occurring immediately, without delay.

[0105] A "sensor module" is a device installed to collect the user's performance data, and has the function of collecting voice, movement, and environmental data.

[0106] "Audio data" refers to information about the characteristics of the sound emitted by an instrument (such as pitch and rhythm).

[0107] "Motion data" refers to information about the movements of the user's hands, fingers, etc. while playing.

[0108] "Environmental data" refers to information such as background sounds and location information of the place where the performance is taking place.

[0109] "Analysis server" refers to a computer system that analyzes the data sent from the sensor module and compares it with a database of expert performances.

[0110] "Expert performance database" refers to a database that stores accurate performance data by experts.

[0111] "Feedback" refers to advice and corrections to the user's performance generated by the analysis server.

[0112] "Terminal" refers to a device for presenting visual and audible feedback to a user.

[0113] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies discrepancies, and provides suggestions for improvement. This system is primarily composed of a user terminal, a sensor module, an analysis server, and a program that links these components. Below, we provide a specific explanation of each element and an example of its operation.

[0114] System Overview

[0115] 1. User Device:

[0116] The device worn by the user has a built-in sensor module that collects performance data in real time.

[0117] When the user starts playing, the terminal sends the collected data to the analysis server.

[0118] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0119] 2. Sensor module:

[0120] The module contains various sensors that collect audio, motion, and environmental data.

[0121] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0122] Environmental sensors collect environmental factors such as background noise and location information.

[0123] 3. Analysis Server:

[0124] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0125] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[0126] Feedback is provided in visual and audio formats to aid user understanding.

[0127] Program processing

[0128] When a user starts playing an instrument, the device collects data through the sensor module. This includes audio data, motion data, and environmental data. The device then transmits the collected data in real time to an analysis server. The analysis server analyzes the received data and detects deviations in pitch and rhythm, as well as errors in finger movements. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance. The analysis server generates feedback for the user based on the identified differences. This feedback includes suggestions for pitch and rhythm corrections, as well as advice on correct finger and hand positioning. The analysis server then transmits the generated feedback to the device, which immediately displays it in the user's field of view.

[0129] For example, when a user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies when certain notes are late or when finger positioning is incorrect. Specific feedback may include text such as "The C4 note is 0.5 seconds late. You need to press the key faster," or an animation showing the correct finger movement.

[0130] For violin practice, when a user begins playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch. Specific feedback includes text such as "The pitch of the E note is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0131] This allows users to efficiently improve their playing skills while receiving expert instruction in real time.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] The user starts playing the instrument. When the user starts playing, the terminal detects the start of the performance.

[0135] Specific behavior:

[0136] When a user presses a piano key or moves a violin bow, the sensor module captures the signal to start playing.

[0137] Step 2:

[0138] The terminal collects performance data, and the sensor module simultaneously collects audio data, movement data, and environmental data.

[0139] Specific behavior:

[0140] Audio sensors record the sounds being played and capture rhythm and pitch information, motion sensors record hand and finger movements, and environmental sensors collect background sounds and position information.

[0141] Input: A performance by the user.

[0142] Output: Audio data, motion data, and environmental data.

[0143] Step 3:

[0144] The device sends the collected data to an analysis server, where it is encoded and transmitted quickly and securely.

[0145] Specific behavior:

[0146] The collected data is divided into small data packets and sent to an analysis server via Wi-Fi or Bluetooth.

[0147] Input: Audio data, motion data, environmental data.

[0148] Output: Data packets received by the analysis server.

[0149] Step 4:

[0150] The analysis server receives the data and begins analysis. The analysis server pre-processes the collected data to compare it with a database of expert performances.

[0151] Specific behavior:

[0152] The information is recovered from the data packets and then passed through an analysis algorithm, which matches it with a database of experts to detect pitch and rhythm discrepancies and movement errors.

[0153] Input: The data packet sent to the analysis server.

[0154] Output: Analysis data used for comparison.

[0155] Step 5:

[0156] The analysis server generates feedback based on the results of comparison with a database of expert performances.

[0157] Specific behavior:

[0158] It detects specific errors such as out-of-tune notes, delayed rhythms, and incorrect finger positioning, and generates text feedback such as "The C4 note is 0.5 seconds late," as well as an animation demonstrating the correct movement.

[0159] Input: Analysis data to be used for comparison.

[0160] Output: Feedback text, visual feedback.

[0161] Step 6:

[0162] The analysis server sends the generated feedback to the terminal, which is encoded and sent to the user terminal in real time.

[0163] Specific behavior:

[0164] The generated feedback data is encoded and transmitted to the user terminal.

[0165] Input: Feedback text, visual feedback.

[0166] Output: Feedback data sent to the device.

[0167] Step 7:

[0168] The device displays feedback to the user, who can then receive guidance and correct their performance in real time.

[0169] Specific behavior:

[0170] Visual feedback (arrows, highlights) is displayed on the AR glasses display, and specific corrections are provided via text message.

[0171] Input: Feedback data sent to the device.

[0172] Output: The feedback that is displayed to the user.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] In today's musical instrument learning environment, individual instruction is often difficult and expensive. Furthermore, there is a lack of timely feedback during self-practice, making it difficult to improve performance skills efficiently. This can slow down progress in musical instrument performance.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, means for sending and receiving data through an application installed on a smartphone, means for accumulating the user's performance data and saving the expert's feedback as a history, and means for managing the user's practice schedule and checking progress. This allows the user to efficiently receive feedback in real time and improve their performance technique.

[0178] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[0179] The "analysis server" is a server that analyzes the performance data sent from the sensor module and compares it with a database of expert performances to identify areas for improvement.

[0180] "Feedback" refers to visual and audible advice and instructions for improvement regarding the user's performance, generated by the analysis server.

[0181] A "terminal" is a device used to provide feedback to the user, and primarily refers to display devices such as smartphones and AR glasses.

[0182] The "application installed on the smartphone" is software that transmits and receives the user's performance data and displays the feedback obtained from the analysis server.

[0183] The "expert performance database" is a database that stores musical instrument performance data of many experts, and is used to compare with the user's performance data.

[0184] A "practice schedule" is a practice plan for playing a musical instrument set by a user, and is a schedule for managing progress.

[0185] "Real-time" means that data is collected and analyzed immediately after the user begins playing.

[0186] "Data transmission and reception" refers to the entire process of sending data from the sensor module to the analysis server and returning the resulting feedback to the user.

[0187] "Saving as history" refers to recording past performance data and analysis results so that they can be referenced later.

[0188] This invention is a system that analyzes performance data in real time and provides feedback when a user plays a musical instrument. The system mainly consists of a sensor module, an analysis server, and a user device (such as a smartphone or AR glasses).

[0189] System configuration

[0190] 1. Sensor module:

[0191] The sensor module collects audio, motion, and environmental data in real time.

[0192] The collected data is sent to an analysis server via an application installed on the smartphone.

[0193] 2. Analysis Server:

[0194] The analysis server analyzes the performance data received from the sensor module and compares it with a database of expert performances.

[0195] It analyzes data in multiple dimensions, including pitch, rhythm, positioning, and finger movement, and generates feedback on the user's playing technique.

[0196] Feedback is generated in visual and auditory form and is stored historically.

[0197] 3. User Device:

[0198] The application installed on the smartphone provides the user with instant feedback from the analysis server.

[0199] Users can improve their performance by receiving real-time feedback while they play.

[0200] The application also allows users to manage their practice schedule and track their progress.

[0201] Processing flow

[0202] When a user starts playing an instrument, the sensor module collects audio, motion, and environmental data in real time, which is then sent to an analytics server via a smartphone.

[0203] The analysis server analyzes the received data and compares it with a database of expert performances, thereby identifying pitch and rhythm discrepancies, finger movement errors, and other issues.

[0204] The analysis server generates feedback for the user based on the identified differences and sends it to the smartphone, allowing the user to modify their performance in real time.

[0205] The device, a smartphone, provides visual and auditory feedback, such as text providing specific improvements and animations showing the correct finger movements.

[0206] Hardware and software used

[0207] Hardware:

[0208] Smartphone: Used for capturing audio and video and displaying feedback.

[0209] Camera and microphone: Built-in smartphone.

[0210] software:

[0211] OpenCV: Video data capture and processing.

[0212] requests module: Sending and receiving data.

[0213] numpy: Processing audio data.

[0214] JSON module: Handling feedback data.

[0215] Specific examples

[0216] Example 1: Piano practice

[0217] 1. The user begins playing the piano.

[0218] 2. The sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[0219] 3. The device sends this data to the analysis server in real time.

[0220] 4. The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[0221] 5. The analysis server generates an animation showing the correct finger movement along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and sends it to the device.

[0222] 6. The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0223] Example prompt sentence:

[0224] "The user plays the piano. The video and audio of the performance are captured and sent to the analysis server. The performance is compared with the performance data of an expert to check the differences, and feedback is generated and displayed."

[0225] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0226] Step 1:

[0227] The user starts playing an instrument. The sensor module collects audio data, motion data, and environmental data. The input is the user's playing sound and movements, which the sensor module captures in real time. The output is the entire collected data.

[0228] Step 2:

[0229] The device (smartphone) transmits data collected from the sensor module to the analysis server in real time. The input is audio data, motion data, and environmental data from the sensor module. The output is a data packet encoded in JSON format. The device uploads this to the analysis server over the network.

[0230] Step 3:

[0231] The analysis server receives the data sent from the device, decodes it, and begins analysis. The input is performance data encoded in JSON format. The analysis server first breaks down the data into different dimensions, such as pitch, rhythm, positioning, and finger movement. The output is the individual analyzed data elements.

[0232] Step 4:

[0233] The analysis server compares the analyzed performance data with the expert performance database. The inputs are data elements such as decomposed pitch, rhythm, positioning, and finger movement, as well as the expert performance database. The server calculates the differences between each element and generates feedback based on these differences. The output is specific feedback data on the user's performance technique.

[0234] Step 5:

[0235] The analysis server sends the generated feedback to the terminal. The input is the feedback data. The output is a data packet for receiving the feedback. The server uploads it to the terminal using the network.

[0236] Step 6:

[0237] The terminal presents the received feedback to the user visually and audibly. The input is feedback data from the analysis server. The terminal converts the feedback into text or animation format for display to the user. The output is the feedback content displayed to the user. The user can use this to modify their performance in real time.

[0238] Step 7:

[0239] The device stores the user's performance data and feedback history. The input is performance data and feedback data. The output is history data that is saved for future reference.

[0240] Step 8:

[0241] The device manages the practice schedule set by the user and checks the progress. The input is the schedule information set by the user. The output is a display of the schedule progress to the user. This allows the user to continue practicing in a planned manner.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[0244] System Overview

[0245] 1. User device (AR glasses):

[0246] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[0247] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[0248] Feedback from the analysis server is displayed in the user's field of view.

[0249] 2. Sensor module:

[0250] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0251] Environmental sensors collect environmental factors such as background noise and location information.

[0252] 3. Emotion-Recognition Camera:

[0253] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[0254] 4. Analysis Server:

[0255] Analyzes data sent from the sensor module and emotion recognition camera.

[0256] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[0257] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[0258] 5. Feedback Generation and Display:

[0259] The analysis server sends the generated feedback to the terminal.

[0260] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[0261] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[0262] Program processing

[0263] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[0264] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[0265] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[0266] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[0267] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[0268] Specific examples

[0269] Example 1: Piano practice

[0270] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0271] The device transmits this data to an analysis server in real time.

[0272] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[0273] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[0274] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0275] Example 2: Violin practice

[0276] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0277] The device transmits this data to an analysis server in real time.

[0278] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[0279] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0280] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0281] The above is a concrete example of the program processing in this system. This system allows users to improve their performance skills efficiently and practice while reducing the psychological burden.

[0282] The processing flow will be explained below.

[0283] Step 1:

[0284] The user puts on the AR glasses and starts the system, which launches the user interface and establishes connections with the sensor module, emotion recognition camera, and analysis server.

[0285] Step 2:

[0286] When a user starts playing an instrument, the device's sensor module starts collecting performance data. Specifically, the audio sensor captures pitch and rhythm, and the motion sensor records hand and finger movements and posture.

[0287] Step 3:

[0288] The device's emotion recognition camera analyzes the user's facial expressions to detect their emotional state in real time, which is determined by analyzing facial features such as smiles and frowns.

[0289] Step 4:

[0290] The device sends collected voice, motion, and emotion data to an analysis server in real time, and the data is securely transferred over the network.

[0291] Step 5:

[0292] The analysis server analyzes pitch and rhythm from the voice data, hand and finger movements and positioning from the motion data, and the user's emotional state (e.g., joy, tension, concentration, etc.) from the emotion data.

[0293] Step 6:

[0294] The analysis server compares the analysis results with a database of expert performances to identify differences between the user's performance and that of the expert, such as pitch discrepancies, rhythmic delays, and positioning errors.

[0295] Step 7:

[0296] The analysis server generates feedback based on the identified discrepancies, including pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual suggestions for specific improvements. The tone and urgency of the feedback is also adjusted based on the user's emotional state.

[0297] Step 8:

[0298] The analysis server sends the generated feedback to the device, which is provided in visual and auditory forms.

[0299] Step 9:

[0300] The device then displays the feedback it receives in the user's field of vision, such as arrows or highlights to indicate correct finger placement, text explanations of pitch discrepancies, and may also add relaxing audio prompts or visual effects depending on the user's emotional state.

[0301] Step 10:

[0302] Based on the provided feedback, the user can then modify their performance and try again, during which time data is collected, analyzed, and feedback is provided again.

[0303] Step 11:

[0304] By repeating this feedback loop, users can improve their performance skills quickly and efficiently, while also practicing with less psychological strain.

[0305] Example 2

[0306] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Conventional music education systems often focus too much on improving the user's performance technique, and often fail to consider the user's emotional state or psychological burden. As a result, if the user continues practicing while feeling tense or impatient, it is difficult to achieve efficient improvement in technique. Furthermore, conventional systems do not provide sufficient real-time feedback, making it difficult for users to make immediate corrections.

[0308] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor module means for collecting the user's musical instrument performance in real time, an analysis server means for analyzing the data collected from the sensor module and the emotion data collected from the emotion recognition camera and comparing it with an expert performance database, and a terminal means for presenting feedback from the analysis server to the user visually and audibly and adjusting the content and tone of the feedback based on the user's emotional state. This allows the user to efficiently improve their performance technique and practice while reducing psychological burden.

[0309] The "sensor module" is a module that includes an audio sensor, a motion sensor, and an environmental sensor for collecting the user's musical instrument performance in real time.

[0310] An "emotion recognition camera" is a camera that analyzes a user's facial expressions and detects the user's emotional state in real time.

[0311] The "analysis server" is a server that has the function of analyzing data collected from the sensor module and emotion recognition camera and comparing it with a database of expert performances.

[0312] The "expert performance database" is a database that stores expert instrument performance data, and is used to compare with the user's performance data.

[0313] "Feedback" refers to information based on improvements and advice generated by the analysis server as a result of analyzing the user's performance data, and is provided to the user visually and audibly via the terminal.

[0314] A "terminal" is a device such as AR glasses worn by a user, which has the ability to provide visual and auditory feedback.

[0315] "Real-time" refers to data being collected, transmitted, and analyzed immediately, without delay.

[0316] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[0317] System Overview

[0318] 1. User device (AR glasses):

[0319] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[0320] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[0321] Feedback from the analysis server is displayed in the user's field of view.

[0322] 2. Sensor module:

[0323] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0324] Environmental sensors collect environmental factors such as background noise and location information.

[0325] 3. Emotion-Recognition Camera:

[0326] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[0327] 4. Analysis Server:

[0328] Analyzes data sent from the sensor module and emotion recognition camera.

[0329] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[0330] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[0331] 5. Feedback Generation and Display:

[0332] The analysis server sends the generated feedback to the terminal.

[0333] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[0334] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[0335] Program processing

[0336] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[0337] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[0338] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[0339] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[0340] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[0341] Specific examples

[0342] Example 1: Piano practice

[0343] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0344] The device transmits this data to an analysis server in real time.

[0345] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[0346] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[0347] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0348] Example 2: Violin practice

[0349] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0350] The device transmits this data to an analysis server in real time.

[0351] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[0352] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0353] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0354] Example prompts for generative AI models

[0355] An example of a prompt is:

[0356] This music education system collects real-time data on the user's performance, compares it with that of an expert, and provides suggestions for improvement. Please generate specific examples of feedback when the user begins to play the piano. Also, please include adjusting the feedback accordingly if the user is nervous.

[0357] By writing prompts in this way, it becomes possible to generate appropriate feedback using a generative AI model. This system allows users to improve their performance skills efficiently and practice while reducing psychological burden.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1: User starts playing

[0360] The system begins to operate when the user starts playing an instrument (e.g., piano or violin).

[0361] Input: User begins playing an instrument.

[0362] Output: Triggering data collection for the sensor module and emotion recognition camera.

[0363] Step 2: Device collects data

[0364] The device's sensor module collects audio and motion data: the audio sensor captures the pitch and rhythm of the instrument, and the motion sensor captures the movement and posture of the hands and fingers.

[0365] The emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0366] Input: User performance data.

[0367] Output: Collected audio, motion, and emotion data.

[0368] Step 3: The device sends the data to the analysis server

[0369] The device sends the collected data to an analysis server in real time over a secure network.

[0370] Input: Collected voice, motion, and emotion data.

[0371] Output: Performance data and emotion data sent to the analysis server.

[0372] Step 4: The analysis server analyzes the data

[0373] The analysis server analyzes the transmitted data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and posture from the motion data, and identifying the user's emotional state based on data from the emotion recognition camera.

[0374] Input: Submitted voice, motion, and emotion data.

[0375] Output: Analysis of pitch, rhythm, hand and finger movements, posture, and emotional state.

[0376] Step 5: The analysis server converts the analysis results into feedback

[0377] Based on the analysis results, the analysis server compares the user's performance data with a database of expert performances, identifies discrepancies, and extracts specific areas for improvement.

[0378] Adjust the content and tone of your feedback based on the user's emotional state.

[0379] Input: Analysis results and expert data.

[0380] Output: Adjusted feedback data (improvements, advice).

[0381] Step 6: The analysis server sends feedback to the device

[0382] The analysis server transmits the generated feedback to the terminal.

[0383] The feedback includes visual guidelines, text, and audio prompts, and is adjusted based on the user's emotional state.

[0384] Input: Feedback data.

[0385] Output: Feedback sent to the device.

[0386] Step 7: The device displays feedback to the user

[0387] The device displays the received feedback in the user's field of view, which may include arrows or highlights indicating the correct finger positions, text explaining pitch discrepancies, and audio guidance.

[0388] The display is adjusted based on the user's emotional state, for example by using relaxing colors and audio prompts.

[0389] Input: Feedback sent to the device.

[0390] Output: Visual and auditory feedback provided to the user.

[0391] The above is the specific processing steps of the program of this system, its detailed operation, and the flow of input and output.

[0392] (Application example 2)

[0393] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0394] While conventional musical instrument performance training systems can provide feedback on a user's performance technique, they are unable to provide feedback that takes into account the user's emotional state. As a result, users are unable to receive appropriate feedback when they are in a tense or stressed situation, making it difficult to improve their performance technique efficiently.

[0395] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, an emotion recognition camera that analyzes the user's emotional state in real time, and a process in the analysis server that adjusts the content of the feedback based on the data collected from the emotion recognition camera. This enables appropriate feedback that takes into account both the user's performance technique and emotional state.

[0396] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[0397] The "analysis server" is a device that analyzes data collected from the sensor module and emotion recognition camera, compares it with a database of expert performances, and generates feedback.

[0398] A "terminal" is a device that provides visual and auditory feedback to a user from the analysis server.

[0399] An "emotion recognition camera" is a camera device that analyzes a user's facial expressions in real time and detects their emotional state.

[0400] The "expert performance database" is a database that accumulates performance data of experts with high skills in playing musical instruments.

[0401] "Feedback" refers to information including suggestions for improvement and advice regarding the user's musical instrument playing.

[0402] "Real-time" means that data collection, analysis, and feedback are instantaneous, with no delay.

[0403] "Audio data" refers to data that records the sounds produced by playing a musical instrument.

[0404] "Motion data" refers to data relating to the movements and posture of hands and fingers while playing an instrument.

[0405] "Environmental data" refers to data related to the performance environment, such as surrounding sounds and position information while playing an instrument.

[0406] "Feedback urgency" is a measure of the priority or importance of feedback.

[0407] "Feedback tone" refers to the way feedback is expressed and the tone of the language used.

[0408] This invention is a system for supporting musical instrument playing education, and is mainly composed of a sensor module, an analysis server, a terminal, and an emotion recognition camera. A specific embodiment of this system will be described below.

[0409] 1. System Configuration

[0410] User device (smart glasses)

[0411] The smart glasses worn by the user are equipped with built-in audio, motion, and environmental sensors, as well as an emotion-recognition camera. While the user plays an instrument, these sensors collect audio, motion, and environmental data in real time. The emotion-recognition camera also analyzes the user's facial expressions to detect their emotional state.

[0412] Sensor Module

[0413] Audio sensor: Captures the pitch and rhythm of musical instruments.

[0414] Motion sensor: Records hand and finger movements, posture, etc.

[0415] Environmental sensors: collect background noise, location information, etc.

[0416] Analysis Server

[0417] The analysis server analyzes the data sent from the smart glasses in real time. Its main functions are as follows:

[0418] Data analysis: Analyzes performance data such as pitch, rhythm, positioning, finger movement, etc. Identifies the user's emotional state based on data from the emotion recognition camera.

[0419] Database matching: Matching with a database of expert performances to identify discrepancies and errors in performance.

[0420] Feedback generation: Feedback is generated based on the analysis results, and the urgency and tone are adjusted taking into account the user's emotional state.

[0421] 2. Providing feedback

[0422] The generated feedback is displayed on the smart glasses' display. The feedback is provided in both visual and auditory forms. For example, a text message indicating pitch misalignment or an animation showing the correct finger movement is displayed. If the user is tense, audio prompts and visual effects are added to help them relax.

[0423] 3. Hardware and Software

[0424] Hardware: Smart glasses (voice sensor, motion sensor, environmental sensor, emotion recognition camera)

[0425] Software: Python, OpenCV (facial expression analysis), Librosa (voice analysis), Scikit-learn (emotion model)

[0426] 4. Specific Examples

[0427] Piano practice:

[0428] As the user plays the piano, sensors in the smart glasses collect voice and motion data, while an emotion-recognition camera analyzes the user's facial expressions.

[0429] The analysis server compares the data and generates text such as "The C4 note is 0.5 seconds late. You need to press the key faster," as well as an animation showing the correct finger movement.

[0430] If the user is nervous, voice guidance such as "Take a deep breath to relax" will also be added.

[0431] Violin Practice:

[0432] As the user plays the violin, data is collected and analyzed in a similar manner.

[0433] The analysis server generates feedback such as, "The E note is out of tune. You need to raise your finger position a little," and also provides guidelines for correcting bow movement.

[0434] Prompt Sentence Examples

[0435] Analyze the user's performance data to identify pitch and rhythm discrepancies. Generate appropriate feedback taking into account the user's emotional state. For example, if the user is playing behind the tempo, say, "You're behind. Please play a little faster." If the user is tense, add advice like, "Play more relaxed."

[0436] In this way, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[0437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0438] Step 1:

[0439] When a user begins playing an instrument, the device's (smart glasses) sensor module collects audio data, motion data, and environmental data. Specifically, the audio sensor captures the instrument's pitch and rhythm, while the motion sensor records hand and finger movements and posture. The environmental sensor also collects ambient noise and location information. This data, along with data from the emotion recognition camera, is sent in real time to an analysis server.

[0440] Input: User's playing sounds, hand and finger movements, surrounding environment

[0441] Output: Audio data, motion data, environmental data, emotion data

[0442] Step 2:

[0443] The server uses Librosa to analyze the received audio data. Specifically, it extracts pitch and rhythm from the audio data and obtains tempo and pitch information. It then analyzes the motion and environmental data. It extracts hand and finger positioning from the motion data and identifies the performance environment from the environmental data.

[0444] Input: Audio data, motion data, environmental data

[0445] Output: Pitch, rhythm, positioning, and playing environment information

[0446] Step 3:

[0447] The server analyzes data from the emotion recognition camera in real time using OpenCV. Specifically, it detects the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) from their facial expressions. The detected emotional information is used as part of the analysis process when generating feedback.

[0448] Input: facial expression data

[0449] Output: Emotional state (happy, angry, sad, surprised, etc.)

[0450] Step 4:

[0451] The server compares the extracted data with a database of expert performances. It identifies pitch discrepancies, rhythmic delays, and errors in hand and finger positioning, and compares the data with pre-stored expert data to highlight any discrepancies. This comparison process identifies specific areas for improvement in the user's performance.

[0452] Input: pitch, rhythm, positioning, expert performance database

[0453] Output: Performance improvements and differences

[0454] Step 5:

[0455] The server generates feedback to provide to the user based on the analysis data and the user's emotional state. The feedback includes specific advice and areas for improvement in the performance. The content of the feedback is also adjusted according to the user's emotional state. For example, if the user is nervous, advice to relax may be added.

[0456] Input: Improvements, Difference Information, Emotional State

[0457] Output: Feedback (improvements, advice)

[0458] Step 6:

[0459] The generated feedback is sent to the device and presented to the user visually and audibly. Visual feedback is displayed on the smart glasses in the form of text or animation, while auditory feedback is provided as audio guidance. For example, arrows or highlights indicating correct finger movements, text messages indicating pitch deviations, and audio guidance for relaxation are also provided.

[0460] Input: Feedback

[0461] Output: Visual feedback, auditory feedback

[0462] Through these steps, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[0463] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0464] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0465] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0466] [Second embodiment]

[0467] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0468] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0469] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0470] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0471] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0472] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0473] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0474] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0475] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0476] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0477] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0478] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0479] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. This system is primarily composed of a user device (AR glasses), a sensor module, an analysis server, and a program that links these devices.

[0480] System Overview

[0481] 1. User device (AR glasses):

[0482] The AR glasses worn by the user have a built-in sensor module that collects performance data in real time.

[0483] When the user starts playing, the terminal sends the collected data to the analysis server.

[0484] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0485] 2. Sensor module:

[0486] The module contains various sensors that collect audio, motion, and environmental data.

[0487] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0488] Environmental sensors collect environmental factors such as background noise and location information.

[0489] 3. Analysis Server:

[0490] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0491] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[0492] Feedback is provided in visual and audio formats to aid user understanding.

[0493] Program processing

[0494] When the user starts playing the instrument, the device collects data through the sensor module, which collects audio data, motion data, and environmental data.

[0495] The terminal transmits the collected data to an analysis server in real time.

[0496] The analysis server analyzes the received data and detects errors in pitch and rhythm, finger movements, etc. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance with that of other users.

[0497] The analysis server generates feedback for the user based on the identified differences, including pitch and rhythm corrections, and correct finger and hand positioning.

[0498] The analytics server sends the generated feedback to the device, which then displays it instantly in the user's field of view, for example by using on-screen arrows or highlights to indicate correct finger placement and by providing specific textual suggestions for improvement.

[0499] Specific examples

[0500] Example 1: Piano practice

[0501] When the user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[0502] The device transmits this data to an analysis server in real time.

[0503] The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[0504] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster."

[0505] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0506] Example 2: Violin practice

[0507] When the user starts playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning).

[0508] The device transmits this data to an analysis server in real time.

[0509] The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch.

[0510] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0511] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0512] The above is a concrete example of the program processing in this system. This system allows users to efficiently improve their playing skills.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] The user puts on the AR glasses and starts the system, which launches the user interface and establishes a connection with the sensor module and analysis server.

[0516] Step 2:

[0517] When a user starts playing an instrument, the device's sensor module starts collecting performance data: audio data from a microphone, motion data from an IMU (inertial measurement unit), and environmental data from environmental sensors.

[0518] Step 3:

[0519] The collected performance data is sent from the device to an analysis server in real time, and the data is transferred securely over the network.

[0520] Step 4:

[0521] The analysis server analyzes the received data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and instrument positioning from the motion data, and using environmental data for noise reduction.

[0522] Step 5:

[0523] Based on the analysis results, the analysis server compares the user's performance with a database of expert performances to identify differences between the expert's performance and the user's, such as pitch discrepancies, rhythmic delays, and incorrect hand and finger positioning.

[0524] Step 6:

[0525] Based on the identified discrepancies, the analysis server generates feedback, which can include pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual instructions for specific improvements.

[0526] Step 7:

[0527] The generated feedback is sent from the analysis server to the device, and is provided in visual and auditory forms.

[0528] Step 8:

[0529] The device displays the received feedback in the user's field of view, for example, arrows or highlights to indicate correct finger placement, text descriptions of pitch discrepancies, etc. Audio feedback may also be played.

[0530] Step 9:

[0531] Based on the provided feedback, the user can then modify their playing technique and try again, during which time data is collected, analyzed, and feedback is provided again.

[0532] Step 10:

[0533] By repeating this feedback loop, users can quickly and efficiently improve their playing skills.

[0534] Example 1

[0535] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0536] Traditional methods of music education require face-to-face lessons with expert instruction, which are subject to time and geographical constraints. Self-practice also increases the risk of continuing to use incorrect techniques, making it difficult to improve efficiently. Furthermore, the lack of real-time feedback slows down the cycle of performance improvement.

[0537] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0538] In this invention, the server includes a sensor module means for collecting the user's performance in real time, an analysis server means for analyzing the data collected from the sensor module means and comparing it with a performance database of experts, and a terminal means for visually and audibly presenting feedback from the analysis server means to the user, thereby enabling the user to receive expert guidance in real time.

[0539] "User" refers to an individual who utilizes the system to play an instrument and receive feedback.

[0540] "Performing" refers to the act of playing music using an instrument.

[0541] "Real-time" refers to processing occurring immediately, without delay.

[0542] A "sensor module" is a device installed to collect the user's performance data, and has the function of collecting voice, movement, and environmental data.

[0543] "Audio data" refers to information about the characteristics of the sound emitted by an instrument (such as pitch and rhythm).

[0544] "Motion data" refers to information about the movements of the user's hands, fingers, etc. while playing.

[0545] "Environmental data" refers to information such as background sounds and location information of the place where the performance is taking place.

[0546] "Analysis server" refers to a computer system that analyzes the data sent from the sensor module and compares it with a database of expert performances.

[0547] "Expert performance database" refers to a database that stores accurate performance data by experts.

[0548] "Feedback" refers to advice and corrections to the user's performance generated by the analysis server.

[0549] "Terminal" refers to a device for presenting visual and audible feedback to a user.

[0550] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies discrepancies, and provides suggestions for improvement. This system is primarily composed of a user terminal, a sensor module, an analysis server, and a program that links these components. Below, we provide a specific explanation of each element and an example of its operation.

[0551] System Overview

[0552] 1. User Device:

[0553] The device worn by the user has a built-in sensor module that collects performance data in real time.

[0554] When the user starts playing, the terminal sends the collected data to the analysis server.

[0555] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0556] 2. Sensor module:

[0557] The module contains various sensors that collect audio, motion, and environmental data.

[0558] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0559] Environmental sensors collect environmental factors such as background noise and location information.

[0560] 3. Analysis Server:

[0561] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0562] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[0563] Feedback is provided in visual and audio formats to aid user understanding.

[0564] Program processing

[0565] When a user starts playing an instrument, the device collects data through the sensor module. This includes audio data, motion data, and environmental data. The device then transmits the collected data in real time to an analysis server. The analysis server analyzes the received data and detects deviations in pitch and rhythm, as well as errors in finger movements. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance. The analysis server generates feedback for the user based on the identified differences. This feedback includes suggestions for pitch and rhythm corrections, as well as advice on correct finger and hand positioning. The analysis server then transmits the generated feedback to the device, which immediately displays it in the user's field of view.

[0566] For example, when a user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies when certain notes are late or when finger positioning is incorrect. Specific feedback may include text such as "The C4 note is 0.5 seconds late. You need to press the key faster," or an animation showing the correct finger movement.

[0567] For violin practice, when a user begins playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch. Specific feedback includes text such as "The pitch of the E note is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0568] This allows users to efficiently improve their playing skills while receiving expert instruction in real time.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] The user starts playing the instrument. When the user starts playing, the terminal detects the start of the performance.

[0572] Specific behavior:

[0573] When a user presses a piano key or moves a violin bow, the sensor module captures the signal to start playing.

[0574] Step 2:

[0575] The terminal collects performance data, and the sensor module simultaneously collects audio data, movement data, and environmental data.

[0576] Specific behavior:

[0577] Audio sensors record the sounds being played and capture rhythm and pitch information, motion sensors record hand and finger movements, and environmental sensors collect background sounds and position information.

[0578] Input: A performance by the user.

[0579] Output: Audio data, motion data, and environmental data.

[0580] Step 3:

[0581] The device sends the collected data to an analysis server, where it is encoded and transmitted quickly and securely.

[0582] Specific behavior:

[0583] The collected data is divided into small data packets and sent to an analysis server via Wi-Fi or Bluetooth.

[0584] Input: Audio data, motion data, environmental data.

[0585] Output: Data packets received by the analysis server.

[0586] Step 4:

[0587] The analysis server receives the data and begins analysis. The analysis server pre-processes the collected data to compare it with a database of expert performances.

[0588] Specific behavior:

[0589] The information is recovered from the data packets and then passed through an analysis algorithm, which matches it with a database of experts to detect pitch and rhythm discrepancies and movement errors.

[0590] Input: The data packet sent to the analysis server.

[0591] Output: Analysis data used for comparison.

[0592] Step 5:

[0593] The analysis server generates feedback based on the results of comparison with a database of expert performances.

[0594] Specific behavior:

[0595] It detects specific errors such as out-of-tune notes, delayed rhythms, and incorrect finger positioning, and generates text feedback such as "The C4 note is 0.5 seconds late," as well as an animation demonstrating the correct movement.

[0596] Input: Analysis data to be used for comparison.

[0597] Output: Feedback text, visual feedback.

[0598] Step 6:

[0599] The analysis server sends the generated feedback to the terminal, which is encoded and sent to the user terminal in real time.

[0600] Specific behavior:

[0601] The generated feedback data is encoded and transmitted to the user terminal.

[0602] Input: Feedback text, visual feedback.

[0603] Output: Feedback data sent to the device.

[0604] Step 7:

[0605] The device displays feedback to the user, who can then receive guidance and correct their performance in real time.

[0606] Specific behavior:

[0607] Visual feedback (arrows, highlights) is displayed on the AR glasses display, and specific corrections are provided via text message.

[0608] Input: Feedback data sent to the device.

[0609] Output: The feedback that is displayed to the user.

[0610] (Application example 1)

[0611] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] In today's musical instrument learning environment, individual instruction is often difficult and expensive. Furthermore, there is a lack of timely feedback during self-practice, making it difficult to improve performance skills efficiently. This can slow down progress in musical instrument performance.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0614] In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, means for sending and receiving data through an application installed on a smartphone, means for accumulating the user's performance data and saving the expert's feedback as a history, and means for managing the user's practice schedule and checking progress. This allows the user to efficiently receive feedback in real time and improve their performance technique.

[0615] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[0616] The "analysis server" is a server that analyzes the performance data sent from the sensor module and compares it with a database of expert performances to identify areas for improvement.

[0617] "Feedback" refers to visual and audible advice and instructions for improvement regarding the user's performance, generated by the analysis server.

[0618] A "terminal" is a device used to provide feedback to the user, and primarily refers to display devices such as smartphones and AR glasses.

[0619] The "application installed on the smartphone" is software that transmits and receives the user's performance data and displays the feedback obtained from the analysis server.

[0620] The "expert performance database" is a database that stores musical instrument performance data of many experts, and is used to compare with the user's performance data.

[0621] A "practice schedule" is a practice plan for playing a musical instrument set by a user, and is a schedule for managing progress.

[0622] "Real-time" means that data is collected and analyzed immediately after the user begins playing.

[0623] "Data transmission and reception" refers to the entire process of sending data from the sensor module to the analysis server and returning the resulting feedback to the user.

[0624] "Saving as history" refers to recording past performance data and analysis results so that they can be referenced later.

[0625] This invention is a system that analyzes performance data in real time and provides feedback when a user plays a musical instrument. The system mainly consists of a sensor module, an analysis server, and a user device (such as a smartphone or AR glasses).

[0626] System configuration

[0627] 1. Sensor module:

[0628] The sensor module collects audio, motion, and environmental data in real time.

[0629] The collected data is sent to an analysis server via an application installed on the smartphone.

[0630] 2. Analysis Server:

[0631] The analysis server analyzes the performance data received from the sensor module and compares it with a database of expert performances.

[0632] It analyzes data in multiple dimensions, including pitch, rhythm, positioning, and finger movement, and generates feedback on the user's playing technique.

[0633] Feedback is generated in visual and auditory form and is stored historically.

[0634] 3. User Device:

[0635] The application installed on the smartphone provides the user with instant feedback from the analysis server.

[0636] Users can improve their performance by receiving real-time feedback while they play.

[0637] The application also allows users to manage their practice schedule and track their progress.

[0638] Processing flow

[0639] When a user starts playing an instrument, the sensor module collects audio, motion, and environmental data in real time, which is then sent to an analytics server via a smartphone.

[0640] The analysis server analyzes the received data and compares it with a database of expert performances, thereby identifying pitch and rhythm discrepancies, finger movement errors, and other issues.

[0641] The analysis server generates feedback for the user based on the identified differences and sends it to the smartphone, allowing the user to modify their performance in real time.

[0642] The device, a smartphone, provides visual and auditory feedback, such as text providing specific improvements and animations showing the correct finger movements.

[0643] Hardware and software used

[0644] Hardware:

[0645] Smartphone: Used for capturing audio and video and displaying feedback.

[0646] Camera and microphone: Built-in smartphone.

[0647] software:

[0648] OpenCV: Video data capture and processing.

[0649] requests module: Sending and receiving data.

[0650] numpy: Processing audio data.

[0651] JSON module: Handling feedback data.

[0652] Specific examples

[0653] Example 1: Piano practice

[0654] 1. The user begins playing the piano.

[0655] 2. The sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[0656] 3. The device sends this data to the analysis server in real time.

[0657] 4. The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[0658] 5. The analysis server generates an animation showing the correct finger movement along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and sends it to the device.

[0659] 6. The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0660] Example prompt sentence:

[0661] "The user plays the piano. The video and audio of the performance are captured and sent to the analysis server. The performance is compared with the performance data of an expert to check the differences, and feedback is generated and displayed."

[0662] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0663] Step 1:

[0664] The user starts playing an instrument. The sensor module collects audio data, motion data, and environmental data. The input is the user's playing sound and movements, which the sensor module captures in real time. The output is the entire collected data.

[0665] Step 2:

[0666] The device (smartphone) transmits data collected from the sensor module to the analysis server in real time. The input is audio data, motion data, and environmental data from the sensor module. The output is a data packet encoded in JSON format. The device uploads this to the analysis server over the network.

[0667] Step 3:

[0668] The analysis server receives the data sent from the device, decodes it, and begins analysis. The input is performance data encoded in JSON format. The analysis server first breaks down the data into different dimensions, such as pitch, rhythm, positioning, and finger movement. The output is the individual analyzed data elements.

[0669] Step 4:

[0670] The analysis server compares the analyzed performance data with the expert performance database. The inputs are data elements such as decomposed pitch, rhythm, positioning, and finger movement, as well as the expert performance database. The server calculates the differences between each element and generates feedback based on these differences. The output is specific feedback data on the user's performance technique.

[0671] Step 5:

[0672] The analysis server sends the generated feedback to the terminal. The input is the feedback data. The output is a data packet for receiving the feedback. The server uploads it to the terminal using the network.

[0673] Step 6:

[0674] The terminal presents the received feedback to the user visually and audibly. The input is feedback data from the analysis server. The terminal converts the feedback into text or animation format for display to the user. The output is the feedback content displayed to the user. The user can use this to modify their performance in real time.

[0675] Step 7:

[0676] The device stores the user's performance data and feedback history. The input is performance data and feedback data. The output is history data that is saved for future reference.

[0677] Step 8:

[0678] The device manages the practice schedule set by the user and checks the progress. The input is the schedule information set by the user. The output is a display of the schedule progress to the user. This allows the user to continue practicing in a planned manner.

[0679] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0680] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[0681] System Overview

[0682] 1. User device (AR glasses):

[0683] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[0684] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[0685] Feedback from the analysis server is displayed in the user's field of view.

[0686] 2. Sensor module:

[0687] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0688] Environmental sensors collect environmental factors such as background noise and location information.

[0689] 3. Emotion-Recognition Camera:

[0690] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[0691] 4. Analysis Server:

[0692] Analyzes data sent from the sensor module and emotion recognition camera.

[0693] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[0694] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[0695] 5. Feedback Generation and Display:

[0696] The analysis server sends the generated feedback to the terminal.

[0697] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[0698] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[0699] Program processing

[0700] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[0701] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[0702] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[0703] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[0704] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[0705] Specific examples

[0706] Example 1: Piano practice

[0707] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0708] The device transmits this data to an analysis server in real time.

[0709] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[0710] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[0711] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0712] Example 2: Violin practice

[0713] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0714] The device transmits this data to an analysis server in real time.

[0715] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[0716] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0717] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0718] The above is a concrete example of the program processing in this system. This system allows users to improve their performance skills efficiently and practice while reducing the psychological burden.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] The user puts on the AR glasses and starts the system, which launches the user interface and establishes connections with the sensor module, emotion recognition camera, and analysis server.

[0722] Step 2:

[0723] When a user starts playing an instrument, the device's sensor module starts collecting performance data. Specifically, the audio sensor captures pitch and rhythm, and the motion sensor records hand and finger movements and posture.

[0724] Step 3:

[0725] The device's emotion recognition camera analyzes the user's facial expressions to detect their emotional state in real time, which is determined by analyzing facial features such as smiles and frowns.

[0726] Step 4:

[0727] The device sends collected voice, motion, and emotion data to an analysis server in real time, and the data is securely transferred over the network.

[0728] Step 5:

[0729] The analysis server analyzes pitch and rhythm from the voice data, hand and finger movements and positioning from the motion data, and the user's emotional state (e.g., joy, tension, concentration, etc.) from the emotion data.

[0730] Step 6:

[0731] The analysis server compares the analysis results with a database of expert performances to identify differences between the user's performance and that of the expert, such as pitch discrepancies, rhythmic delays, and positioning errors.

[0732] Step 7:

[0733] The analysis server generates feedback based on the identified discrepancies, including pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual suggestions for specific improvements. The tone and urgency of the feedback is also adjusted based on the user's emotional state.

[0734] Step 8:

[0735] The analysis server sends the generated feedback to the device, which is provided in visual and auditory forms.

[0736] Step 9:

[0737] The device then displays the feedback it receives in the user's field of vision, such as arrows or highlights to indicate correct finger placement, text explanations of pitch discrepancies, and may also add relaxing audio prompts or visual effects depending on the user's emotional state.

[0738] Step 10:

[0739] Based on the provided feedback, the user can then modify their performance and try again, during which time data is collected, analyzed, and feedback is provided again.

[0740] Step 11:

[0741] By repeating this feedback loop, users can improve their performance skills quickly and efficiently, while also practicing with less psychological strain.

[0742] Example 2

[0743] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0744] Conventional music education systems often focus too much on improving the user's performance technique, and often fail to consider the user's emotional state or psychological burden. As a result, if the user continues practicing while feeling tense or impatient, it is difficult to achieve efficient improvement in technique. Furthermore, conventional systems do not provide sufficient real-time feedback, making it difficult for users to make immediate corrections.

[0745] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor module means for collecting the user's musical instrument performance in real time, an analysis server means for analyzing the data collected from the sensor module and the emotion data collected from the emotion recognition camera and comparing it with an expert performance database, and a terminal means for presenting feedback from the analysis server to the user visually and audibly and adjusting the content and tone of the feedback based on the user's emotional state. This allows the user to efficiently improve their performance technique and practice while reducing psychological burden.

[0746] The "sensor module" is a module that includes an audio sensor, a motion sensor, and an environmental sensor for collecting the user's musical instrument performance in real time.

[0747] An "emotion recognition camera" is a camera that analyzes a user's facial expressions and detects the user's emotional state in real time.

[0748] The "analysis server" is a server that has the function of analyzing data collected from the sensor module and emotion recognition camera and comparing it with a database of expert performances.

[0749] The "expert performance database" is a database that stores expert instrument performance data, and is used to compare with the user's performance data.

[0750] "Feedback" refers to information based on improvements and advice generated by the analysis server as a result of analyzing the user's performance data, and is provided to the user visually and audibly via the terminal.

[0751] A "terminal" is a device such as AR glasses worn by a user, which has the ability to provide visual and auditory feedback.

[0752] "Real-time" refers to data being collected, transmitted, and analyzed immediately, without delay.

[0753] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[0754] System Overview

[0755] 1. User device (AR glasses):

[0756] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[0757] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[0758] Feedback from the analysis server is displayed in the user's field of view.

[0759] 2. Sensor module:

[0760] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0761] Environmental sensors collect environmental factors such as background noise and location information.

[0762] 3. Emotion-Recognition Camera:

[0763] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[0764] 4. Analysis Server:

[0765] Analyzes data sent from the sensor module and emotion recognition camera.

[0766] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[0767] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[0768] 5. Feedback Generation and Display:

[0769] The analysis server sends the generated feedback to the terminal.

[0770] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[0771] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[0772] Program processing

[0773] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[0774] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[0775] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[0776] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[0777] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[0778] Specific examples

[0779] Example 1: Piano practice

[0780] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0781] The device transmits this data to an analysis server in real time.

[0782] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[0783] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[0784] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0785] Example 2: Violin practice

[0786] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0787] The device transmits this data to an analysis server in real time.

[0788] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[0789] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0790] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0791] Example prompts for generative AI models

[0792] An example of a prompt is:

[0793] This music education system collects real-time data on the user's performance, compares it with that of an expert, and provides suggestions for improvement. Please generate specific examples of feedback when the user begins to play the piano. Also, please include adjusting the feedback accordingly if the user is nervous.

[0794] By writing prompts in this way, it becomes possible to generate appropriate feedback using a generative AI model. This system allows users to improve their performance skills efficiently and practice while reducing psychological burden.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1: User starts playing

[0797] The system begins to operate when the user starts playing an instrument (e.g., piano or violin).

[0798] Input: User begins playing an instrument.

[0799] Output: Triggering data collection for the sensor module and emotion recognition camera.

[0800] Step 2: Device collects data

[0801] The device's sensor module collects audio and motion data: the audio sensor captures the pitch and rhythm of the instrument, and the motion sensor captures the movement and posture of the hands and fingers.

[0802] The emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[0803] Input: User performance data.

[0804] Output: Collected audio, motion, and emotion data.

[0805] Step 3: The device sends the data to the analysis server

[0806] The device sends the collected data to an analysis server in real time over a secure network.

[0807] Input: Collected voice, motion, and emotion data.

[0808] Output: Performance data and emotion data sent to the analysis server.

[0809] Step 4: The analysis server analyzes the data

[0810] The analysis server analyzes the transmitted data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and posture from the motion data, and identifying the user's emotional state based on data from the emotion recognition camera.

[0811] Input: Submitted voice, motion, and emotion data.

[0812] Output: Analysis of pitch, rhythm, hand and finger movements, posture, and emotional state.

[0813] Step 5: The analysis server converts the analysis results into feedback

[0814] Based on the analysis results, the analysis server compares the user's performance data with a database of expert performances, identifies discrepancies, and extracts specific areas for improvement.

[0815] Adjust the content and tone of your feedback based on the user's emotional state.

[0816] Input: Analysis results and expert data.

[0817] Output: Adjusted feedback data (improvements, advice).

[0818] Step 6: The analysis server sends feedback to the device

[0819] The analysis server transmits the generated feedback to the terminal.

[0820] The feedback includes visual guidelines, text, and audio prompts, and is adjusted based on the user's emotional state.

[0821] Input: Feedback data.

[0822] Output: Feedback sent to the device.

[0823] Step 7: The device displays feedback to the user

[0824] The device displays the received feedback in the user's field of view, which may include arrows or highlights indicating the correct finger positions, text explaining pitch discrepancies, and audio guidance.

[0825] The display is adjusted based on the user's emotional state, for example by using relaxing colors and audio prompts.

[0826] Input: Feedback sent to the device.

[0827] Output: Visual and auditory feedback provided to the user.

[0828] The above is the specific processing steps of the program of this system, its detailed operation, and the flow of input and output.

[0829] (Application example 2)

[0830] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0831] While conventional musical instrument performance training systems can provide feedback on a user's performance technique, they are unable to provide feedback that takes into account the user's emotional state. As a result, users are unable to receive appropriate feedback when they are in a tense or stressed situation, making it difficult to improve their performance technique efficiently.

[0832] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, an emotion recognition camera that analyzes the user's emotional state in real time, and a process in the analysis server that adjusts the content of the feedback based on the data collected from the emotion recognition camera. This enables appropriate feedback that takes into account both the user's performance technique and emotional state.

[0833] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[0834] The "analysis server" is a device that analyzes data collected from the sensor module and emotion recognition camera, compares it with a database of expert performances, and generates feedback.

[0835] A "terminal" is a device that provides visual and auditory feedback to a user from the analysis server.

[0836] An "emotion recognition camera" is a camera device that analyzes a user's facial expressions in real time and detects their emotional state.

[0837] The "expert performance database" is a database that accumulates performance data of experts with high skills in playing musical instruments.

[0838] "Feedback" refers to information including suggestions for improvement and advice regarding the user's musical instrument playing.

[0839] "Real-time" means that data collection, analysis, and feedback are instantaneous, with no delay.

[0840] "Audio data" refers to data that records the sounds produced by playing a musical instrument.

[0841] "Motion data" refers to data relating to the movements and posture of hands and fingers while playing an instrument.

[0842] "Environmental data" refers to data related to the performance environment, such as surrounding sounds and position information while playing an instrument.

[0843] "Feedback urgency" is a measure of the priority or importance of feedback.

[0844] "Feedback tone" refers to the way feedback is expressed and the tone of the language used.

[0845] This invention is a system for supporting musical instrument playing education, and is mainly composed of a sensor module, an analysis server, a terminal, and an emotion recognition camera. A specific embodiment of this system will be described below.

[0846] 1. System Configuration

[0847] User device (smart glasses)

[0848] The smart glasses worn by the user are equipped with built-in audio, motion, and environmental sensors, as well as an emotion-recognition camera. While the user plays an instrument, these sensors collect audio, motion, and environmental data in real time. The emotion-recognition camera also analyzes the user's facial expressions to detect their emotional state.

[0849] Sensor Module

[0850] Audio sensor: Captures the pitch and rhythm of musical instruments.

[0851] Motion sensor: Records hand and finger movements, posture, etc.

[0852] Environmental sensors: collect background noise, location information, etc.

[0853] Analysis Server

[0854] The analysis server analyzes the data sent from the smart glasses in real time. Its main functions are as follows:

[0855] Data analysis: Analyzes performance data such as pitch, rhythm, positioning, finger movement, etc. Identifies the user's emotional state based on data from the emotion recognition camera.

[0856] Database matching: Matching with a database of expert performances to identify discrepancies and errors in performance.

[0857] Feedback generation: Feedback is generated based on the analysis results, and the urgency and tone are adjusted taking into account the user's emotional state.

[0858] 2. Providing feedback

[0859] The generated feedback is displayed on the smart glasses' display. The feedback is provided in both visual and auditory forms. For example, a text message indicating pitch misalignment or an animation showing the correct finger movement is displayed. If the user is tense, audio prompts and visual effects are added to help them relax.

[0860] 3. Hardware and Software

[0861] Hardware: Smart glasses (voice sensor, motion sensor, environmental sensor, emotion recognition camera)

[0862] Software: Python, OpenCV (facial expression analysis), Librosa (voice analysis), Scikit-learn (emotion model)

[0863] 4. Specific Examples

[0864] Piano practice:

[0865] As the user plays the piano, sensors in the smart glasses collect voice and motion data, while an emotion-recognition camera analyzes the user's facial expressions.

[0866] The analysis server compares the data and generates text such as "The C4 note is 0.5 seconds late. You need to press the key faster," as well as an animation showing the correct finger movement.

[0867] If the user is nervous, voice guidance such as "Take a deep breath to relax" will also be added.

[0868] Violin Practice:

[0869] As the user plays the violin, data is collected and analyzed in a similar manner.

[0870] The analysis server generates feedback such as, "The E note is out of tune. You need to raise your finger position a little," and also provides guidelines for correcting bow movement.

[0871] Prompt Sentence Examples

[0872] Analyze the user's performance data to identify pitch and rhythm discrepancies. Generate appropriate feedback taking into account the user's emotional state. For example, if the user is playing behind the tempo, say, "You're behind. Please play a little faster." If the user is tense, add advice like, "Play more relaxed."

[0873] In this way, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[0874] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0875] Step 1:

[0876] When a user begins playing an instrument, the device's (smart glasses) sensor module collects audio data, motion data, and environmental data. Specifically, the audio sensor captures the instrument's pitch and rhythm, while the motion sensor records hand and finger movements and posture. The environmental sensor also collects ambient noise and location information. This data, along with data from the emotion recognition camera, is sent in real time to an analysis server.

[0877] Input: User's playing sounds, hand and finger movements, surrounding environment

[0878] Output: Audio data, motion data, environmental data, emotion data

[0879] Step 2:

[0880] The server uses Librosa to analyze the received audio data. Specifically, it extracts pitch and rhythm from the audio data and obtains tempo and pitch information. It then analyzes the motion and environmental data. It extracts hand and finger positioning from the motion data and identifies the performance environment from the environmental data.

[0881] Input: Audio data, motion data, environmental data

[0882] Output: Pitch, rhythm, positioning, and playing environment information

[0883] Step 3:

[0884] The server analyzes data from the emotion recognition camera in real time using OpenCV. Specifically, it detects the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) from their facial expressions. The detected emotional information is used as part of the analysis process when generating feedback.

[0885] Input: facial expression data

[0886] Output: Emotional state (happy, angry, sad, surprised, etc.)

[0887] Step 4:

[0888] The server compares the extracted data with a database of expert performances. It identifies pitch discrepancies, rhythmic delays, and errors in hand and finger positioning, and compares the data with pre-stored expert data to highlight any discrepancies. This comparison process identifies specific areas for improvement in the user's performance.

[0889] Input: pitch, rhythm, positioning, expert performance database

[0890] Output: Performance improvements and differences

[0891] Step 5:

[0892] The server generates feedback to provide to the user based on the analysis data and the user's emotional state. The feedback includes specific advice and areas for improvement in the performance. The content of the feedback is also adjusted according to the user's emotional state. For example, if the user is nervous, advice to relax may be added.

[0893] Input: Improvements, Difference Information, Emotional State

[0894] Output: Feedback (improvements, advice)

[0895] Step 6:

[0896] The generated feedback is sent to the device and presented to the user visually and audibly. Visual feedback is displayed on the smart glasses in the form of text or animation, while auditory feedback is provided as audio guidance. For example, arrows or highlights indicating correct finger movements, text messages indicating pitch deviations, and audio guidance for relaxation are also provided.

[0897] Input: Feedback

[0898] Output: Visual feedback, auditory feedback

[0899] Through these steps, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[0900] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0901] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0902] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0903] [Third embodiment]

[0904] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0905] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0906] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0907] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0908] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0909] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0910] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0911] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0912] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0913] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0914] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0915] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0916] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. This system is primarily composed of a user device (AR glasses), a sensor module, an analysis server, and a program that links these devices.

[0917] System Overview

[0918] 1. User device (AR glasses):

[0919] The AR glasses worn by the user have a built-in sensor module that collects performance data in real time.

[0920] When the user starts playing, the terminal sends the collected data to the analysis server.

[0921] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0922] 2. Sensor module:

[0923] The module contains various sensors that collect audio, motion, and environmental data.

[0924] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0925] Environmental sensors collect environmental factors such as background noise and location information.

[0926] 3. Analysis Server:

[0927] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0928] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[0929] Feedback is provided in visual and audio formats to aid user understanding.

[0930] Program processing

[0931] When the user starts playing the instrument, the device collects data through the sensor module, which collects audio data, motion data, and environmental data.

[0932] The terminal transmits the collected data to an analysis server in real time.

[0933] The analysis server analyzes the received data and detects errors in pitch and rhythm, finger movements, etc. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance with that of other users.

[0934] The analysis server generates feedback for the user based on the identified differences, including pitch and rhythm corrections, and correct finger and hand positioning.

[0935] The analytics server sends the generated feedback to the device, which then displays it instantly in the user's field of view, for example by using on-screen arrows or highlights to indicate correct finger placement and by providing specific textual suggestions for improvement.

[0936] Specific examples

[0937] Example 1: Piano practice

[0938] When the user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[0939] The device transmits this data to an analysis server in real time.

[0940] The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[0941] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster."

[0942] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[0943] Example 2: Violin practice

[0944] When the user starts playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning).

[0945] The device transmits this data to an analysis server in real time.

[0946] The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch.

[0947] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[0948] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[0949] The above is a concrete example of the program processing in this system. This system allows users to efficiently improve their playing skills.

[0950] The processing flow will be explained below.

[0951] Step 1:

[0952] The user puts on the AR glasses and starts the system, which launches the user interface and establishes a connection with the sensor module and analysis server.

[0953] Step 2:

[0954] When a user starts playing an instrument, the device's sensor module starts collecting performance data: audio data from a microphone, motion data from an IMU (inertial measurement unit), and environmental data from environmental sensors.

[0955] Step 3:

[0956] The collected performance data is sent from the device to an analysis server in real time, and the data is transferred securely over the network.

[0957] Step 4:

[0958] The analysis server analyzes the received data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and instrument positioning from the motion data, and using environmental data for noise reduction.

[0959] Step 5:

[0960] Based on the analysis results, the analysis server compares the user's performance with a database of expert performances to identify differences between the expert's performance and the user's, such as pitch discrepancies, rhythmic delays, and incorrect hand and finger positioning.

[0961] Step 6:

[0962] Based on the identified discrepancies, the analysis server generates feedback, which can include pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual instructions for specific improvements.

[0963] Step 7:

[0964] The generated feedback is sent from the analysis server to the device, and is provided in visual and auditory forms.

[0965] Step 8:

[0966] The device displays the received feedback in the user's field of view, for example, arrows or highlights to indicate correct finger placement, text descriptions of pitch discrepancies, etc. Audio feedback may also be played.

[0967] Step 9:

[0968] Based on the provided feedback, the user can then modify their playing technique and try again, during which time data is collected, analyzed, and feedback is provided again.

[0969] Step 10:

[0970] By repeating this feedback loop, users can quickly and efficiently improve their playing skills.

[0971] Example 1

[0972] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0973] Traditional methods of music education require face-to-face lessons with expert instruction, which are subject to time and geographical constraints. Self-practice also increases the risk of continuing to use incorrect techniques, making it difficult to improve efficiently. Furthermore, the lack of real-time feedback slows down the cycle of performance improvement.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0975] In this invention, the server includes a sensor module means for collecting the user's performance in real time, an analysis server means for analyzing the data collected from the sensor module means and comparing it with a performance database of experts, and a terminal means for visually and audibly presenting feedback from the analysis server means to the user, thereby enabling the user to receive expert guidance in real time.

[0976] "User" refers to an individual who utilizes the system to play an instrument and receive feedback.

[0977] "Performing" refers to the act of playing music using an instrument.

[0978] "Real-time" refers to processing occurring immediately, without delay.

[0979] A "sensor module" is a device installed to collect the user's performance data, and has the function of collecting voice, movement, and environmental data.

[0980] "Audio data" refers to information about the characteristics of the sound emitted by an instrument (such as pitch and rhythm).

[0981] "Motion data" refers to information about the movements of the user's hands, fingers, etc. while playing.

[0982] "Environmental data" refers to information such as background sounds and location information of the place where the performance is taking place.

[0983] "Analysis server" refers to a computer system that analyzes the data sent from the sensor module and compares it with a database of expert performances.

[0984] "Expert performance database" refers to a database that stores accurate performance data by experts.

[0985] "Feedback" refers to advice and corrections to the user's performance generated by the analysis server.

[0986] "Terminal" refers to a device for presenting visual and audible feedback to a user.

[0987] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies discrepancies, and provides suggestions for improvement. This system is primarily composed of a user terminal, a sensor module, an analysis server, and a program that links these components. Below, we provide a specific explanation of each element and an example of its operation.

[0988] System Overview

[0989] 1. User Device:

[0990] The device worn by the user has a built-in sensor module that collects performance data in real time.

[0991] When the user starts playing, the terminal sends the collected data to the analysis server.

[0992] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[0993] 2. Sensor module:

[0994] The module contains various sensors that collect audio, motion, and environmental data.

[0995] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[0996] Environmental sensors collect environmental factors such as background noise and location information.

[0997] 3. Analysis Server:

[0998] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[0999] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[1000] Feedback is provided in visual and audio formats to aid user understanding.

[1001] Program processing

[1002] When a user starts playing an instrument, the device collects data through the sensor module. This includes audio data, motion data, and environmental data. The device then transmits the collected data in real time to an analysis server. The analysis server analyzes the received data and detects deviations in pitch and rhythm, as well as errors in finger movements. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance. The analysis server generates feedback for the user based on the identified differences. This feedback includes suggestions for pitch and rhythm corrections, as well as advice on correct finger and hand positioning. The analysis server then transmits the generated feedback to the device, which immediately displays it in the user's field of view.

[1003] For example, when a user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies when certain notes are late or when finger positioning is incorrect. Specific feedback may include text such as "The C4 note is 0.5 seconds late. You need to press the key faster," or an animation showing the correct finger movement.

[1004] For violin practice, when a user begins playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch. Specific feedback includes text such as "The pitch of the E note is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1005] This allows users to efficiently improve their playing skills while receiving expert instruction in real time.

[1006] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] The user starts playing the instrument. When the user starts playing, the terminal detects the start of the performance.

[1009] Specific behavior:

[1010] When a user presses a piano key or moves a violin bow, the sensor module captures the signal to start playing.

[1011] Step 2:

[1012] The terminal collects performance data, and the sensor module simultaneously collects audio data, movement data, and environmental data.

[1013] Specific behavior:

[1014] Audio sensors record the sounds being played and capture rhythm and pitch information, motion sensors record hand and finger movements, and environmental sensors collect background sounds and position information.

[1015] Input: A performance by the user.

[1016] Output: Audio data, motion data, and environmental data.

[1017] Step 3:

[1018] The device sends the collected data to an analysis server, where it is encoded and transmitted quickly and securely.

[1019] Specific behavior:

[1020] The collected data is divided into small data packets and sent to an analysis server via Wi-Fi or Bluetooth.

[1021] Input: Audio data, motion data, environmental data.

[1022] Output: Data packets received by the analysis server.

[1023] Step 4:

[1024] The analysis server receives the data and begins analysis. The analysis server pre-processes the collected data to compare it with a database of expert performances.

[1025] Specific behavior:

[1026] The information is recovered from the data packets and then passed through an analysis algorithm, which matches it with a database of experts to detect pitch and rhythm discrepancies and movement errors.

[1027] Input: The data packet sent to the analysis server.

[1028] Output: Analysis data used for comparison.

[1029] Step 5:

[1030] The analysis server generates feedback based on the results of comparison with a database of expert performances.

[1031] Specific behavior:

[1032] It detects specific errors such as out-of-tune notes, delayed rhythms, and incorrect finger positioning, and generates text feedback such as "The C4 note is 0.5 seconds late," as well as an animation demonstrating the correct movement.

[1033] Input: Analysis data to be used for comparison.

[1034] Output: Feedback text, visual feedback.

[1035] Step 6:

[1036] The analysis server sends the generated feedback to the terminal, which is encoded and sent to the user terminal in real time.

[1037] Specific behavior:

[1038] The generated feedback data is encoded and transmitted to the user terminal.

[1039] Input: Feedback text, visual feedback.

[1040] Output: Feedback data sent to the device.

[1041] Step 7:

[1042] The device displays feedback to the user, who can then receive guidance and correct their performance in real time.

[1043] Specific behavior:

[1044] Visual feedback (arrows, highlights) is displayed on the AR glasses display, and specific corrections are provided via text message.

[1045] Input: Feedback data sent to the device.

[1046] Output: The feedback that is displayed to the user.

[1047] (Application example 1)

[1048] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1049] In today's musical instrument learning environment, individual instruction is often difficult and expensive. Furthermore, there is a lack of timely feedback during self-practice, making it difficult to improve performance skills efficiently. This can slow down progress in musical instrument performance.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1051] In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, means for sending and receiving data through an application installed on a smartphone, means for accumulating the user's performance data and saving the expert's feedback as a history, and means for managing the user's practice schedule and checking progress. This allows the user to efficiently receive feedback in real time and improve their performance technique.

[1052] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[1053] The "analysis server" is a server that analyzes the performance data sent from the sensor module and compares it with a database of expert performances to identify areas for improvement.

[1054] "Feedback" refers to visual and audible advice and instructions for improvement regarding the user's performance, generated by the analysis server.

[1055] A "terminal" is a device used to provide feedback to the user, and primarily refers to display devices such as smartphones and AR glasses.

[1056] The "application installed on the smartphone" is software that transmits and receives the user's performance data and displays the feedback obtained from the analysis server.

[1057] The "expert performance database" is a database that stores musical instrument performance data of many experts, and is used to compare with the user's performance data.

[1058] A "practice schedule" is a practice plan for playing a musical instrument set by a user, and is a schedule for managing progress.

[1059] "Real-time" means that data is collected and analyzed immediately after the user begins playing.

[1060] "Data transmission and reception" refers to the entire process of sending data from the sensor module to the analysis server and returning the resulting feedback to the user.

[1061] "Saving as history" refers to recording past performance data and analysis results so that they can be referenced later.

[1062] This invention is a system that analyzes performance data in real time and provides feedback when a user plays a musical instrument. The system mainly consists of a sensor module, an analysis server, and a user device (such as a smartphone or AR glasses).

[1063] System configuration

[1064] 1. Sensor module:

[1065] The sensor module collects audio, motion, and environmental data in real time.

[1066] The collected data is sent to an analysis server via an application installed on the smartphone.

[1067] 2. Analysis Server:

[1068] The analysis server analyzes the performance data received from the sensor module and compares it with a database of expert performances.

[1069] It analyzes data in multiple dimensions, including pitch, rhythm, positioning, and finger movement, and generates feedback on the user's playing technique.

[1070] Feedback is generated in visual and auditory form and is stored historically.

[1071] 3. User Device:

[1072] The application installed on the smartphone provides the user with instant feedback from the analysis server.

[1073] Users can improve their performance by receiving real-time feedback while they play.

[1074] The application also allows users to manage their practice schedule and track their progress.

[1075] Processing flow

[1076] When a user starts playing an instrument, the sensor module collects audio, motion, and environmental data in real time, which is then sent to an analytics server via a smartphone.

[1077] The analysis server analyzes the received data and compares it with a database of expert performances, thereby identifying pitch and rhythm discrepancies, finger movement errors, and other issues.

[1078] The analysis server generates feedback for the user based on the identified differences and sends it to the smartphone, allowing the user to modify their performance in real time.

[1079] The device, a smartphone, provides visual and auditory feedback, such as text providing specific improvements and animations showing the correct finger movements.

[1080] Hardware and software used

[1081] Hardware:

[1082] Smartphone: Used for capturing audio and video and displaying feedback.

[1083] Camera and microphone: Built-in smartphone.

[1084] software:

[1085] OpenCV: Video data capture and processing.

[1086] requests module: Sending and receiving data.

[1087] numpy: Processing audio data.

[1088] JSON module: Handling feedback data.

[1089] Specific examples

[1090] Example 1: Piano practice

[1091] 1. The user begins playing the piano.

[1092] 2. The sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[1093] 3. The device sends this data to the analysis server in real time.

[1094] 4. The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[1095] 5. The analysis server generates an animation showing the correct finger movement along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and sends it to the device.

[1096] 6. The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1097] Example prompt sentence:

[1098] "The user plays the piano. The video and audio of the performance are captured and sent to the analysis server. The performance is compared with the performance data of an expert to check the differences, and feedback is generated and displayed."

[1099] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1100] Step 1:

[1101] The user starts playing an instrument. The sensor module collects audio data, motion data, and environmental data. The input is the user's playing sound and movements, which the sensor module captures in real time. The output is the entire collected data.

[1102] Step 2:

[1103] The device (smartphone) transmits data collected from the sensor module to the analysis server in real time. The input is audio data, motion data, and environmental data from the sensor module. The output is a data packet encoded in JSON format. The device uploads this to the analysis server over the network.

[1104] Step 3:

[1105] The analysis server receives the data sent from the device, decodes it, and begins analysis. The input is performance data encoded in JSON format. The analysis server first breaks down the data into different dimensions, such as pitch, rhythm, positioning, and finger movement. The output is the individual analyzed data elements.

[1106] Step 4:

[1107] The analysis server compares the analyzed performance data with the expert performance database. The inputs are data elements such as decomposed pitch, rhythm, positioning, and finger movement, as well as the expert performance database. The server calculates the differences between each element and generates feedback based on these differences. The output is specific feedback data on the user's performance technique.

[1108] Step 5:

[1109] The analysis server sends the generated feedback to the terminal. The input is the feedback data. The output is a data packet for receiving the feedback. The server uploads it to the terminal using the network.

[1110] Step 6:

[1111] The terminal presents the received feedback to the user visually and audibly. The input is feedback data from the analysis server. The terminal converts the feedback into text or animation format for display to the user. The output is the feedback content displayed to the user. The user can use this to modify their performance in real time.

[1112] Step 7:

[1113] The device stores the user's performance data and feedback history. The input is performance data and feedback data. The output is history data that is saved for future reference.

[1114] Step 8:

[1115] The device manages the practice schedule set by the user and checks the progress. The input is the schedule information set by the user. The output is a display of the schedule progress to the user. This allows the user to continue practicing in a planned manner.

[1116] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1117] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[1118] System Overview

[1119] 1. User device (AR glasses):

[1120] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[1121] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[1122] Feedback from the analysis server is displayed in the user's field of view.

[1123] 2. Sensor module:

[1124] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1125] Environmental sensors collect environmental factors such as background noise and location information.

[1126] 3. Emotion-Recognition Camera:

[1127] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[1128] 4. Analysis Server:

[1129] Analyzes data sent from the sensor module and emotion recognition camera.

[1130] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[1131] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[1132] 5. Feedback Generation and Display:

[1133] The analysis server sends the generated feedback to the terminal.

[1134] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[1135] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[1136] Program processing

[1137] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[1138] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[1139] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[1140] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[1141] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[1142] Specific examples

[1143] Example 1: Piano practice

[1144] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1145] The device transmits this data to an analysis server in real time.

[1146] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[1147] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[1148] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1149] Example 2: Violin practice

[1150] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1151] The device transmits this data to an analysis server in real time.

[1152] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[1153] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1154] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[1155] The above is a concrete example of the program processing in this system. This system allows users to improve their performance skills efficiently and practice while reducing the psychological burden.

[1156] The processing flow will be explained below.

[1157] Step 1:

[1158] The user puts on the AR glasses and starts the system, which launches the user interface and establishes connections with the sensor module, emotion recognition camera, and analysis server.

[1159] Step 2:

[1160] When a user starts playing an instrument, the device's sensor module starts collecting performance data. Specifically, the audio sensor captures pitch and rhythm, and the motion sensor records hand and finger movements and posture.

[1161] Step 3:

[1162] The device's emotion recognition camera analyzes the user's facial expressions to detect their emotional state in real time, which is determined by analyzing facial features such as smiles and frowns.

[1163] Step 4:

[1164] The device sends collected voice, motion, and emotion data to an analysis server in real time, and the data is securely transferred over the network.

[1165] Step 5:

[1166] The analysis server analyzes pitch and rhythm from the voice data, hand and finger movements and positioning from the motion data, and the user's emotional state (e.g., joy, tension, concentration, etc.) from the emotion data.

[1167] Step 6:

[1168] The analysis server compares the analysis results with a database of expert performances to identify differences between the user's performance and that of the expert, such as pitch discrepancies, rhythmic delays, and positioning errors.

[1169] Step 7:

[1170] The analysis server generates feedback based on the identified discrepancies, including pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual suggestions for specific improvements. The tone and urgency of the feedback is also adjusted based on the user's emotional state.

[1171] Step 8:

[1172] The analysis server sends the generated feedback to the device, which is provided in visual and auditory forms.

[1173] Step 9:

[1174] The device then displays the feedback it receives in the user's field of vision, such as arrows or highlights to indicate correct finger placement, text explanations of pitch discrepancies, and may also add relaxing audio prompts or visual effects depending on the user's emotional state.

[1175] Step 10:

[1176] Based on the provided feedback, the user can then modify their performance and try again, during which time data is collected, analyzed, and feedback is provided again.

[1177] Step 11:

[1178] By repeating this feedback loop, users can improve their performance skills quickly and efficiently, while also practicing with less psychological strain.

[1179] Example 2

[1180] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] Conventional music education systems often focus too much on improving the user's performance technique, and often fail to consider the user's emotional state or psychological burden. As a result, if the user continues practicing while feeling tense or impatient, it is difficult to achieve efficient improvement in technique. Furthermore, conventional systems do not provide sufficient real-time feedback, making it difficult for users to make immediate corrections.

[1182] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor module means for collecting the user's musical instrument performance in real time, an analysis server means for analyzing the data collected from the sensor module and the emotion data collected from the emotion recognition camera and comparing it with an expert performance database, and a terminal means for presenting feedback from the analysis server to the user visually and audibly and adjusting the content and tone of the feedback based on the user's emotional state. This allows the user to efficiently improve their performance technique and practice while reducing psychological burden.

[1183] The "sensor module" is a module that includes an audio sensor, a motion sensor, and an environmental sensor for collecting the user's musical instrument performance in real time.

[1184] An "emotion recognition camera" is a camera that analyzes a user's facial expressions and detects the user's emotional state in real time.

[1185] The "analysis server" is a server that has the function of analyzing data collected from the sensor module and emotion recognition camera and comparing it with a database of expert performances.

[1186] The "expert performance database" is a database that stores expert instrument performance data, and is used to compare with the user's performance data.

[1187] "Feedback" refers to information based on improvements and advice generated by the analysis server as a result of analyzing the user's performance data, and is provided to the user visually and audibly via the terminal.

[1188] A "terminal" is a device such as AR glasses worn by a user, which has the ability to provide visual and auditory feedback.

[1189] "Real-time" refers to data being collected, transmitted, and analyzed immediately, without delay.

[1190] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[1191] System Overview

[1192] 1. User device (AR glasses):

[1193] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[1194] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[1195] Feedback from the analysis server is displayed in the user's field of view.

[1196] 2. Sensor module:

[1197] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1198] Environmental sensors collect environmental factors such as background noise and location information.

[1199] 3. Emotion-Recognition Camera:

[1200] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[1201] 4. Analysis Server:

[1202] Analyzes data sent from the sensor module and emotion recognition camera.

[1203] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[1204] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[1205] 5. Feedback Generation and Display:

[1206] The analysis server sends the generated feedback to the terminal.

[1207] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[1208] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[1209] Program processing

[1210] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[1211] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[1212] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[1213] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[1214] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[1215] Specific examples

[1216] Example 1: Piano practice

[1217] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1218] The device transmits this data to an analysis server in real time.

[1219] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[1220] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[1221] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1222] Example 2: Violin practice

[1223] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1224] The device transmits this data to an analysis server in real time.

[1225] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[1226] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1227] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[1228] Example prompts for generative AI models

[1229] An example of a prompt is:

[1230] This music education system collects real-time data on the user's performance, compares it with that of an expert, and provides suggestions for improvement. Please generate specific examples of feedback when the user begins to play the piano. Also, please include adjusting the feedback accordingly if the user is nervous.

[1231] By writing prompts in this way, it becomes possible to generate appropriate feedback using a generative AI model. This system allows users to improve their performance skills efficiently and practice while reducing psychological burden.

[1232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1233] Step 1: User starts playing

[1234] The system begins to operate when the user starts playing an instrument (e.g., piano or violin).

[1235] Input: User begins playing an instrument.

[1236] Output: Triggering data collection for the sensor module and emotion recognition camera.

[1237] Step 2: Device collects data

[1238] The device's sensor module collects audio and motion data: the audio sensor captures the pitch and rhythm of the instrument, and the motion sensor captures the movement and posture of the hands and fingers.

[1239] The emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1240] Input: User performance data.

[1241] Output: Collected audio, motion, and emotion data.

[1242] Step 3: The device sends the data to the analysis server

[1243] The device sends the collected data to an analysis server in real time over a secure network.

[1244] Input: Collected voice, motion, and emotion data.

[1245] Output: Performance data and emotion data sent to the analysis server.

[1246] Step 4: The analysis server analyzes the data

[1247] The analysis server analyzes the transmitted data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and posture from the motion data, and identifying the user's emotional state based on data from the emotion recognition camera.

[1248] Input: Submitted voice, motion, and emotion data.

[1249] Output: Analysis of pitch, rhythm, hand and finger movements, posture, and emotional state.

[1250] Step 5: The analysis server converts the analysis results into feedback

[1251] Based on the analysis results, the analysis server compares the user's performance data with a database of expert performances, identifies discrepancies, and extracts specific areas for improvement.

[1252] Adjust the content and tone of your feedback based on the user's emotional state.

[1253] Input: Analysis results and expert data.

[1254] Output: Adjusted feedback data (improvements, advice).

[1255] Step 6: The analysis server sends feedback to the device

[1256] The analysis server transmits the generated feedback to the terminal.

[1257] The feedback includes visual guidelines, text, and audio prompts, and is adjusted based on the user's emotional state.

[1258] Input: Feedback data.

[1259] Output: Feedback sent to the device.

[1260] Step 7: The device displays feedback to the user

[1261] The device displays the received feedback in the user's field of view, which may include arrows or highlights indicating the correct finger positions, text explaining pitch discrepancies, and audio guidance.

[1262] The display is adjusted based on the user's emotional state, for example by using relaxing colors and audio prompts.

[1263] Input: Feedback sent to the device.

[1264] Output: Visual and auditory feedback provided to the user.

[1265] The above is the specific processing steps of the program of this system, its detailed operation, and the flow of input and output.

[1266] (Application example 2)

[1267] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1268] While conventional musical instrument performance training systems can provide feedback on a user's performance technique, they are unable to provide feedback that takes into account the user's emotional state. As a result, users are unable to receive appropriate feedback when they are in a tense or stressed situation, making it difficult to improve their performance technique efficiently.

[1269] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, an emotion recognition camera that analyzes the user's emotional state in real time, and a process in the analysis server that adjusts the content of the feedback based on the data collected from the emotion recognition camera. This enables appropriate feedback that takes into account both the user's performance technique and emotional state.

[1270] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[1271] The "analysis server" is a device that analyzes data collected from the sensor module and emotion recognition camera, compares it with a database of expert performances, and generates feedback.

[1272] A "terminal" is a device that provides visual and auditory feedback to a user from the analysis server.

[1273] An "emotion recognition camera" is a camera device that analyzes a user's facial expressions in real time and detects their emotional state.

[1274] The "expert performance database" is a database that accumulates performance data of experts with high skills in playing musical instruments.

[1275] "Feedback" refers to information including suggestions for improvement and advice regarding the user's musical instrument playing.

[1276] "Real-time" means that data collection, analysis, and feedback are instantaneous, with no delay.

[1277] "Audio data" refers to data that records the sounds produced by playing a musical instrument.

[1278] "Motion data" refers to data relating to the movements and posture of hands and fingers while playing an instrument.

[1279] "Environmental data" refers to data related to the performance environment, such as surrounding sounds and position information while playing an instrument.

[1280] "Feedback urgency" is a measure of the priority or importance of feedback.

[1281] "Feedback tone" refers to the way feedback is expressed and the tone of the language used.

[1282] This invention is a system for supporting musical instrument playing education, and is mainly composed of a sensor module, an analysis server, a terminal, and an emotion recognition camera. A specific embodiment of this system will be described below.

[1283] 1. System Configuration

[1284] User device (smart glasses)

[1285] The smart glasses worn by the user are equipped with built-in audio, motion, and environmental sensors, as well as an emotion-recognition camera. While the user plays an instrument, these sensors collect audio, motion, and environmental data in real time. The emotion-recognition camera also analyzes the user's facial expressions to detect their emotional state.

[1286] Sensor Module

[1287] Audio sensor: Captures the pitch and rhythm of musical instruments.

[1288] Motion sensor: Records hand and finger movements, posture, etc.

[1289] Environmental sensors: collect background noise, location information, etc.

[1290] Analysis Server

[1291] The analysis server analyzes the data sent from the smart glasses in real time. Its main functions are as follows:

[1292] Data analysis: Analyzes performance data such as pitch, rhythm, positioning, finger movement, etc. Identifies the user's emotional state based on data from the emotion recognition camera.

[1293] Database matching: Matching with a database of expert performances to identify discrepancies and errors in performance.

[1294] Feedback generation: Feedback is generated based on the analysis results, and the urgency and tone are adjusted taking into account the user's emotional state.

[1295] 2. Providing feedback

[1296] The generated feedback is displayed on the smart glasses' display. The feedback is provided in both visual and auditory forms. For example, a text message indicating pitch misalignment or an animation showing the correct finger movement is displayed. If the user is tense, audio prompts and visual effects are added to help them relax.

[1297] 3. Hardware and Software

[1298] Hardware: Smart glasses (voice sensor, motion sensor, environmental sensor, emotion recognition camera)

[1299] Software: Python, OpenCV (facial expression analysis), Librosa (voice analysis), Scikit-learn (emotion model)

[1300] 4. Specific Examples

[1301] Piano practice:

[1302] As the user plays the piano, sensors in the smart glasses collect voice and motion data, while an emotion-recognition camera analyzes the user's facial expressions.

[1303] The analysis server compares the data and generates text such as "The C4 note is 0.5 seconds late. You need to press the key faster," as well as an animation showing the correct finger movement.

[1304] If the user is nervous, voice guidance such as "Take a deep breath to relax" will also be added.

[1305] Violin Practice:

[1306] As the user plays the violin, data is collected and analyzed in a similar manner.

[1307] The analysis server generates feedback such as, "The E note is out of tune. You need to raise your finger position a little," and also provides guidelines for correcting bow movement.

[1308] Prompt Sentence Examples

[1309] Analyze the user's performance data to identify pitch and rhythm discrepancies. Generate appropriate feedback taking into account the user's emotional state. For example, if the user is playing behind the tempo, say, "You're behind. Please play a little faster." If the user is tense, add advice like, "Play more relaxed."

[1310] In this way, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[1311] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1312] Step 1:

[1313] When a user begins playing an instrument, the device's (smart glasses) sensor module collects audio data, motion data, and environmental data. Specifically, the audio sensor captures the instrument's pitch and rhythm, while the motion sensor records hand and finger movements and posture. The environmental sensor also collects ambient noise and location information. This data, along with data from the emotion recognition camera, is sent in real time to an analysis server.

[1314] Input: User's playing sounds, hand and finger movements, surrounding environment

[1315] Output: Audio data, motion data, environmental data, emotion data

[1316] Step 2:

[1317] The server uses Librosa to analyze the received audio data. Specifically, it extracts pitch and rhythm from the audio data and obtains tempo and pitch information. It then analyzes the motion and environmental data. It extracts hand and finger positioning from the motion data and identifies the performance environment from the environmental data.

[1318] Input: Audio data, motion data, environmental data

[1319] Output: Pitch, rhythm, positioning, and playing environment information

[1320] Step 3:

[1321] The server analyzes data from the emotion recognition camera in real time using OpenCV. Specifically, it detects the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) from their facial expressions. The detected emotional information is used as part of the analysis process when generating feedback.

[1322] Input: facial expression data

[1323] Output: Emotional state (happy, angry, sad, surprised, etc.)

[1324] Step 4:

[1325] The server compares the extracted data with a database of expert performances. It identifies pitch discrepancies, rhythmic delays, and errors in hand and finger positioning, and compares the data with pre-stored expert data to highlight any discrepancies. This comparison process identifies specific areas for improvement in the user's performance.

[1326] Input: pitch, rhythm, positioning, expert performance database

[1327] Output: Performance improvements and differences

[1328] Step 5:

[1329] The server generates feedback to provide to the user based on the analysis data and the user's emotional state. The feedback includes specific advice and areas for improvement in the performance. The content of the feedback is also adjusted according to the user's emotional state. For example, if the user is nervous, advice to relax may be added.

[1330] Input: Improvements, Difference Information, Emotional State

[1331] Output: Feedback (improvements, advice)

[1332] Step 6:

[1333] The generated feedback is sent to the device and presented to the user visually and audibly. Visual feedback is displayed on the smart glasses in the form of text or animation, while auditory feedback is provided as audio guidance. For example, arrows or highlights indicating correct finger movements, text messages indicating pitch deviations, and audio guidance for relaxation are also provided.

[1334] Input: Feedback

[1335] Output: Visual feedback, auditory feedback

[1336] Through these steps, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[1337] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1338] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1339] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1340] [Fourth embodiment]

[1341] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1342] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1343] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1344] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1345] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1346] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1347] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1348] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1349] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1350] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1351] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1352] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1353] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1354] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. This system is primarily composed of a user device (AR glasses), a sensor module, an analysis server, and a program that links these devices.

[1355] System Overview

[1356] 1. User device (AR glasses):

[1357] The AR glasses worn by the user have a built-in sensor module that collects performance data in real time.

[1358] When the user starts playing, the terminal sends the collected data to the analysis server.

[1359] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[1360] 2. Sensor module:

[1361] The module contains various sensors that collect audio, motion, and environmental data.

[1362] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1363] Environmental sensors collect environmental factors such as background noise and location information.

[1364] 3. Analysis Server:

[1365] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[1366] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[1367] Feedback is provided in visual and audio formats to aid user understanding.

[1368] Program processing

[1369] When the user starts playing the instrument, the device collects data through the sensor module, which collects audio data, motion data, and environmental data.

[1370] The terminal transmits the collected data to an analysis server in real time.

[1371] The analysis server analyzes the received data and detects errors in pitch and rhythm, finger movements, etc. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance with that of other users.

[1372] The analysis server generates feedback for the user based on the identified differences, including pitch and rhythm corrections, and correct finger and hand positioning.

[1373] The analytics server sends the generated feedback to the device, which then displays it instantly in the user's field of view, for example by using on-screen arrows or highlights to indicate correct finger placement and by providing specific textual suggestions for improvement.

[1374] Specific examples

[1375] Example 1: Piano practice

[1376] When the user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[1377] The device transmits this data to an analysis server in real time.

[1378] The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[1379] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster."

[1380] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1381] Example 2: Violin practice

[1382] When the user starts playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning).

[1383] The device transmits this data to an analysis server in real time.

[1384] The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch.

[1385] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1386] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[1387] The above is a concrete example of the program processing in this system. This system allows users to efficiently improve their playing skills.

[1388] The processing flow will be explained below.

[1389] Step 1:

[1390] The user puts on the AR glasses and starts the system, which launches the user interface and establishes a connection with the sensor module and analysis server.

[1391] Step 2:

[1392] When a user starts playing an instrument, the device's sensor module starts collecting performance data: audio data from a microphone, motion data from an IMU (inertial measurement unit), and environmental data from environmental sensors.

[1393] Step 3:

[1394] The collected performance data is sent from the device to an analysis server in real time, and the data is transferred securely over the network.

[1395] Step 4:

[1396] The analysis server analyzes the received data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and instrument positioning from the motion data, and using environmental data for noise reduction.

[1397] Step 5:

[1398] Based on the analysis results, the analysis server compares the user's performance with a database of expert performances to identify differences between the expert's performance and the user's, such as pitch discrepancies, rhythmic delays, and incorrect hand and finger positioning.

[1399] Step 6:

[1400] Based on the identified discrepancies, the analysis server generates feedback, which can include pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual instructions for specific improvements.

[1401] Step 7:

[1402] The generated feedback is sent from the analysis server to the device, and is provided in visual and auditory forms.

[1403] Step 8:

[1404] The device displays the received feedback in the user's field of view, for example, arrows or highlights to indicate correct finger placement, text descriptions of pitch discrepancies, etc. Audio feedback may also be played.

[1405] Step 9:

[1406] Based on the provided feedback, the user can then modify their playing technique and try again, during which time data is collected, analyzed, and feedback is provided again.

[1407] Step 10:

[1408] By repeating this feedback loop, users can quickly and efficiently improve their playing skills.

[1409] Example 1

[1410] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1411] Traditional methods of music education require face-to-face lessons with expert instruction, which are subject to time and geographical constraints. Self-practice also increases the risk of continuing to use incorrect techniques, making it difficult to improve efficiently. Furthermore, the lack of real-time feedback slows down the cycle of performance improvement.

[1412] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1413] In this invention, the server includes a sensor module means for collecting the user's performance in real time, an analysis server means for analyzing the data collected from the sensor module means and comparing it with a performance database of experts, and a terminal means for visually and audibly presenting feedback from the analysis server means to the user, thereby enabling the user to receive expert guidance in real time.

[1414] "User" refers to an individual who utilizes the system to play an instrument and receive feedback.

[1415] "Performing" refers to the act of playing music using an instrument.

[1416] "Real-time" refers to processing occurring immediately, without delay.

[1417] A "sensor module" is a device installed to collect the user's performance data, and has the function of collecting voice, movement, and environmental data.

[1418] "Audio data" refers to information about the characteristics of the sound emitted by an instrument (such as pitch and rhythm).

[1419] "Motion data" refers to information about the movements of the user's hands, fingers, etc. while playing.

[1420] "Environmental data" refers to information such as background sounds and location information of the place where the performance is taking place.

[1421] "Analysis server" refers to a computer system that analyzes the data sent from the sensor module and compares it with a database of expert performances.

[1422] "Expert performance database" refers to a database that stores accurate performance data by experts.

[1423] "Feedback" refers to advice and corrections to the user's performance generated by the analysis server.

[1424] "Terminal" refers to a device for presenting visual and audible feedback to a user.

[1425] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies discrepancies, and provides suggestions for improvement. This system is primarily composed of a user terminal, a sensor module, an analysis server, and a program that links these components. Below, we provide a specific explanation of each element and an example of its operation.

[1426] System Overview

[1427] 1. User Device:

[1428] The device worn by the user has a built-in sensor module that collects performance data in real time.

[1429] When the user starts playing, the terminal sends the collected data to the analysis server.

[1430] Once feedback is received from the analytics server, it is displayed directly in the user's field of view.

[1431] 2. Sensor module:

[1432] The module contains various sensors that collect audio, motion, and environmental data.

[1433] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1434] Environmental sensors collect environmental factors such as background noise and location information.

[1435] 3. Analysis Server:

[1436] The performance data sent from the sensor module is analyzed and compared with a database of expert performances.

[1437] It analyzes pitch, rhythm, positioning, finger movement, and other aspects in multiple dimensions to generate feedback on the user's playing technique.

[1438] Feedback is provided in visual and audio formats to aid user understanding.

[1439] Program processing

[1440] When a user starts playing an instrument, the device collects data through the sensor module. This includes audio data, motion data, and environmental data. The device then transmits the collected data in real time to an analysis server. The analysis server analyzes the received data and detects deviations in pitch and rhythm, as well as errors in finger movements. It then compares the data with a database of expert performances and identifies shortcomings and areas for improvement by comparing the user's performance. The analysis server generates feedback for the user based on the identified differences. This feedback includes suggestions for pitch and rhythm corrections, as well as advice on correct finger and hand positioning. The analysis server then transmits the generated feedback to the device, which immediately displays it in the user's field of view.

[1441] For example, when a user starts playing the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies when certain notes are late or when finger positioning is incorrect. Specific feedback may include text such as "The C4 note is 0.5 seconds late. You need to press the key faster," or an animation showing the correct finger movement.

[1442] For violin practice, when a user begins playing the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning). The device then transmits this data in real time to an analysis server. The analysis server analyzes the data and identifies any irregularities in the bow movement or pitch. Specific feedback includes text such as "The pitch of the E note is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1443] This allows users to efficiently improve their playing skills while receiving expert instruction in real time.

[1444] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1445] Step 1:

[1446] The user starts playing the instrument. When the user starts playing, the terminal detects the start of the performance.

[1447] Specific behavior:

[1448] When a user presses a piano key or moves a violin bow, the sensor module captures the signal to start playing.

[1449] Step 2:

[1450] The terminal collects performance data, and the sensor module simultaneously collects audio data, movement data, and environmental data.

[1451] Specific behavior:

[1452] Audio sensors record the sounds being played and capture rhythm and pitch information, motion sensors record hand and finger movements, and environmental sensors collect background sounds and position information.

[1453] Input: A performance by the user.

[1454] Output: Audio data, motion data, and environmental data.

[1455] Step 3:

[1456] The device sends the collected data to an analysis server, where it is encoded and transmitted quickly and securely.

[1457] Specific behavior:

[1458] The collected data is divided into small data packets and sent to an analysis server via Wi-Fi or Bluetooth.

[1459] Input: Audio data, motion data, environmental data.

[1460] Output: Data packets received by the analysis server.

[1461] Step 4:

[1462] The analysis server receives the data and begins analysis. The analysis server pre-processes the collected data to compare it with a database of expert performances.

[1463] Specific behavior:

[1464] The information is recovered from the data packets and then passed through an analysis algorithm, which matches it with a database of experts to detect pitch and rhythm discrepancies and movement errors.

[1465] Input: The data packet sent to the analysis server.

[1466] Output: Analysis data used for comparison.

[1467] Step 5:

[1468] The analysis server generates feedback based on the results of comparison with a database of expert performances.

[1469] Specific behavior:

[1470] It detects specific errors such as out-of-tune notes, delayed rhythms, and incorrect finger positioning, and generates text feedback such as "The C4 note is 0.5 seconds late," as well as an animation demonstrating the correct movement.

[1471] Input: Analysis data to be used for comparison.

[1472] Output: Feedback text, visual feedback.

[1473] Step 6:

[1474] The analysis server sends the generated feedback to the terminal, which is encoded and sent to the user terminal in real time.

[1475] Specific behavior:

[1476] The generated feedback data is encoded and transmitted to the user terminal.

[1477] Input: Feedback text, visual feedback.

[1478] Output: Feedback data sent to the device.

[1479] Step 7:

[1480] The device displays feedback to the user, who can then receive guidance and correct their performance in real time.

[1481] Specific behavior:

[1482] Visual feedback (arrows, highlights) is displayed on the AR glasses display, and specific corrections are provided via text message.

[1483] Input: Feedback data sent to the device.

[1484] Output: The feedback that is displayed to the user.

[1485] (Application example 1)

[1486] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1487] In today's musical instrument learning environment, individual instruction is often difficult and expensive. Furthermore, there is a lack of timely feedback during self-practice, making it difficult to improve performance skills efficiently. This can slow down progress in musical instrument performance.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1489] In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, means for sending and receiving data through an application installed on a smartphone, means for accumulating the user's performance data and saving the expert's feedback as a history, and means for managing the user's practice schedule and checking progress. This allows the user to efficiently receive feedback in real time and improve their performance technique.

[1490] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[1491] The "analysis server" is a server that analyzes the performance data sent from the sensor module and compares it with a database of expert performances to identify areas for improvement.

[1492] "Feedback" refers to visual and audible advice and instructions for improvement regarding the user's performance, generated by the analysis server.

[1493] A "terminal" is a device used to provide feedback to the user, and primarily refers to display devices such as smartphones and AR glasses.

[1494] The "application installed on the smartphone" is software that transmits and receives the user's performance data and displays the feedback obtained from the analysis server.

[1495] The "expert performance database" is a database that stores musical instrument performance data of many experts, and is used to compare with the user's performance data.

[1496] A "practice schedule" is a practice plan for playing a musical instrument set by a user, and is a schedule for managing progress.

[1497] "Real-time" means that data is collected and analyzed immediately after the user begins playing.

[1498] "Data transmission and reception" refers to the entire process of sending data from the sensor module to the analysis server and returning the resulting feedback to the user.

[1499] "Saving as history" refers to recording past performance data and analysis results so that they can be referenced later.

[1500] This invention is a system that analyzes performance data in real time and provides feedback when a user plays a musical instrument. The system mainly consists of a sensor module, an analysis server, and a user device (such as a smartphone or AR glasses).

[1501] System configuration

[1502] 1. Sensor module:

[1503] The sensor module collects audio, motion, and environmental data in real time.

[1504] The collected data is sent to an analysis server via an application installed on the smartphone.

[1505] 2. Analysis Server:

[1506] The analysis server analyzes the performance data received from the sensor module and compares it with a database of expert performances.

[1507] It analyzes data in multiple dimensions, including pitch, rhythm, positioning, and finger movement, and generates feedback on the user's playing technique.

[1508] Feedback is generated in visual and auditory form and is stored historically.

[1509] 3. User Device:

[1510] The application installed on the smartphone provides the user with instant feedback from the analysis server.

[1511] Users can improve their performance by receiving real-time feedback while they play.

[1512] The application also allows users to manage their practice schedule and track their progress.

[1513] Processing flow

[1514] When a user starts playing an instrument, the sensor module collects audio, motion, and environmental data in real time, which is then sent to an analytics server via a smartphone.

[1515] The analysis server analyzes the received data and compares it with a database of expert performances, thereby identifying pitch and rhythm discrepancies, finger movement errors, and other issues.

[1516] The analysis server generates feedback for the user based on the identified differences and sends it to the smartphone, allowing the user to modify their performance in real time.

[1517] The device, a smartphone, provides visual and auditory feedback, such as text providing specific improvements and animations showing the correct finger movements.

[1518] Hardware and software used

[1519] Hardware:

[1520] Smartphone: Used for capturing audio and video and displaying feedback.

[1521] Camera and microphone: Built-in smartphone.

[1522] software:

[1523] OpenCV: Video data capture and processing.

[1524] requests module: Sending and receiving data.

[1525] numpy: Processing audio data.

[1526] JSON module: Handling feedback data.

[1527] Specific examples

[1528] Example 1: Piano practice

[1529] 1. The user begins playing the piano.

[1530] 2. The sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements).

[1531] 3. The device sends this data to the analysis server in real time.

[1532] 4. The analytics server analyzes the data and identifies when certain notes are delayed or when finger positioning is incorrect.

[1533] 5. The analysis server generates an animation showing the correct finger movement along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and sends it to the device.

[1534] 6. The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1535] Example prompt sentence:

[1536] "The user plays the piano. The video and audio of the performance are captured and sent to the analysis server. The performance is compared with the performance data of an expert to check the differences, and feedback is generated and displayed."

[1537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1538] Step 1:

[1539] The user starts playing an instrument. The sensor module collects audio data, motion data, and environmental data. The input is the user's playing sound and movements, which the sensor module captures in real time. The output is the entire collected data.

[1540] Step 2:

[1541] The device (smartphone) transmits data collected from the sensor module to the analysis server in real time. The input is audio data, motion data, and environmental data from the sensor module. The output is a data packet encoded in JSON format. The device uploads this to the analysis server over the network.

[1542] Step 3:

[1543] The analysis server receives the data sent from the device, decodes it, and begins analysis. The input is performance data encoded in JSON format. The analysis server first breaks down the data into different dimensions, such as pitch, rhythm, positioning, and finger movement. The output is the individual analyzed data elements.

[1544] Step 4:

[1545] The analysis server compares the analyzed performance data with the expert performance database. The inputs are data elements such as decomposed pitch, rhythm, positioning, and finger movement, as well as the expert performance database. The server calculates the differences between each element and generates feedback based on these differences. The output is specific feedback data on the user's performance technique.

[1546] Step 5:

[1547] The analysis server sends the generated feedback to the terminal. The input is the feedback data. The output is a data packet for receiving the feedback. The server uploads it to the terminal using the network.

[1548] Step 6:

[1549] The terminal presents the received feedback to the user visually and audibly. The input is feedback data from the analysis server. The terminal converts the feedback into text or animation format for display to the user. The output is the feedback content displayed to the user. The user can use this to modify their performance in real time.

[1550] Step 7:

[1551] The device stores the user's performance data and feedback history. The input is performance data and feedback data. The output is history data that is saved for future reference.

[1552] Step 8:

[1553] The device manages the practice schedule set by the user and checks the progress. The input is the schedule information set by the user. The output is a display of the schedule progress to the user. This allows the user to continue practicing in a planned manner.

[1554] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1555] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[1556] System Overview

[1557] 1. User device (AR glasses):

[1558] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[1559] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[1560] Feedback from the analysis server is displayed in the user's field of view.

[1561] 2. Sensor module:

[1562] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1563] Environmental sensors collect environmental factors such as background noise and location information.

[1564] 3. Emotion-Recognition Camera:

[1565] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[1566] 4. Analysis Server:

[1567] Analyzes data sent from the sensor module and emotion recognition camera.

[1568] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[1569] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[1570] 5. Feedback Generation and Display:

[1571] The analysis server sends the generated feedback to the terminal.

[1572] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[1573] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[1574] Program processing

[1575] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[1576] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[1577] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[1578] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[1579] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[1580] Specific examples

[1581] Example 1: Piano practice

[1582] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1583] The device transmits this data to an analysis server in real time.

[1584] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[1585] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[1586] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1587] Example 2: Violin practice

[1588] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1589] The device transmits this data to an analysis server in real time.

[1590] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[1591] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1592] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[1593] The above is a concrete example of the program processing in this system. This system allows users to improve their performance skills efficiently and practice while reducing the psychological burden.

[1594] The processing flow will be explained below.

[1595] Step 1:

[1596] The user puts on the AR glasses and starts the system, which launches the user interface and establishes connections with the sensor module, emotion recognition camera, and analysis server.

[1597] Step 2:

[1598] When a user starts playing an instrument, the device's sensor module starts collecting performance data. Specifically, the audio sensor captures pitch and rhythm, and the motion sensor records hand and finger movements and posture.

[1599] Step 3:

[1600] The device's emotion recognition camera analyzes the user's facial expressions to detect their emotional state in real time, which is determined by analyzing facial features such as smiles and frowns.

[1601] Step 4:

[1602] The device sends collected voice, motion, and emotion data to an analysis server in real time, and the data is securely transferred over the network.

[1603] Step 5:

[1604] The analysis server analyzes pitch and rhythm from the voice data, hand and finger movements and positioning from the motion data, and the user's emotional state (e.g., joy, tension, concentration, etc.) from the emotion data.

[1605] Step 6:

[1606] The analysis server compares the analysis results with a database of expert performances to identify differences between the user's performance and that of the expert, such as pitch discrepancies, rhythmic delays, and positioning errors.

[1607] Step 7:

[1608] The analysis server generates feedback based on the identified discrepancies, including pitch and rhythm corrections, guidelines for correct hand and finger positioning, and textual suggestions for specific improvements. The tone and urgency of the feedback is also adjusted based on the user's emotional state.

[1609] Step 8:

[1610] The analysis server sends the generated feedback to the device, which is provided in visual and auditory forms.

[1611] Step 9:

[1612] The device then displays the feedback it receives in the user's field of vision, such as arrows or highlights to indicate correct finger placement, text explanations of pitch discrepancies, and may also add relaxing audio prompts or visual effects depending on the user's emotional state.

[1613] Step 10:

[1614] Based on the provided feedback, the user can then modify their performance and try again, during which time data is collected, analyzed, and feedback is provided again.

[1615] Step 11:

[1616] By repeating this feedback loop, users can improve their performance skills quickly and efficiently, while also practicing with less psychological strain.

[1617] Example 2

[1618] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1619] Conventional music education systems often focus too much on improving the user's performance technique, and often fail to consider the user's emotional state or psychological burden. As a result, if the user continues practicing while feeling tense or impatient, it is difficult to achieve efficient improvement in technique. Furthermore, conventional systems do not provide sufficient real-time feedback, making it difficult for users to make immediate corrections.

[1620] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor module means for collecting the user's musical instrument performance in real time, an analysis server means for analyzing the data collected from the sensor module and the emotion data collected from the emotion recognition camera and comparing it with an expert performance database, and a terminal means for presenting feedback from the analysis server to the user visually and audibly and adjusting the content and tone of the feedback based on the user's emotional state. This allows the user to efficiently improve their performance technique and practice while reducing psychological burden.

[1621] The "sensor module" is a module that includes an audio sensor, a motion sensor, and an environmental sensor for collecting the user's musical instrument performance in real time.

[1622] An "emotion recognition camera" is a camera that analyzes a user's facial expressions and detects the user's emotional state in real time.

[1623] The "analysis server" is a server that has the function of analyzing data collected from the sensor module and emotion recognition camera and comparing it with a database of expert performances.

[1624] The "expert performance database" is a database that stores expert instrument performance data, and is used to compare with the user's performance data.

[1625] "Feedback" refers to information based on improvements and advice generated by the analysis server as a result of analyzing the user's performance data, and is provided to the user visually and audibly via the terminal.

[1626] A "terminal" is a device such as AR glasses worn by a user, which has the ability to provide visual and auditory feedback.

[1627] "Real-time" refers to data being collected, transmitted, and analyzed immediately, without delay.

[1628] This invention is a music education system that analyzes a user's musical instrument performance in real time, compares it with performance data from experts, identifies differences, and provides suggestions for improvement. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and includes a function to adjust feedback based on the user's emotional state.

[1629] System Overview

[1630] 1. User device (AR glasses):

[1631] The AR glasses worn by the user are equipped with a sensor module and an emotion recognition camera.

[1632] Once the performance begins, the device collects data using the sensor module and emotion recognition camera and sends it to an analysis server in real time.

[1633] Feedback from the analysis server is displayed in the user's field of view.

[1634] 2. Sensor module:

[1635] Audio sensors capture pitch and rhythm, while motion sensors record hand and finger movements and posture.

[1636] Environmental sensors collect environmental factors such as background noise and location information.

[1637] 3. Emotion-Recognition Camera:

[1638] Facial expression recognition technology is used to analyze the user's facial expressions and detect the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) in real time.

[1639] 4. Analysis Server:

[1640] Analyzes data sent from the sensor module and emotion recognition camera.

[1641] In addition to pitch, rhythm, positioning, finger movement, etc., feedback is generated taking into account the user's emotional state.

[1642] The performance is compared with a database of expert performances to identify any differences between the user's performance and the performance.

[1643] 5. Feedback Generation and Display:

[1644] The analysis server sends the generated feedback to the terminal.

[1645] The device provides visual and auditory feedback to the user, and the content and presentation of the feedback are adjusted according to the user's emotional state.

[1646] For example, if a user is nervous, use relaxing audio prompts and soft visual effects.

[1647] Program processing

[1648] When the user starts playing, the device's sensor module and emotion recognition camera begin collecting performance and emotion data, which is then sent to the analysis server in real time.

[1649] The analysis server extracts pitch and rhythm from the audio data, analyzes hand and finger movements and posture from the motion data, and identifies the user's emotional state based on data from the emotion recognition camera.

[1650] The analysis server compares the performance with a database of expert musicians to identify pitch inaccuracies, rhythmic delays, and incorrect hand and finger positioning, while simultaneously adjusting the urgency and tone of the feedback based on the user's emotional state.

[1651] The analysis server generates feedback and sends it to the device, including specific improvements and advice, in the form of visual guidelines, text, and audio prompts.

[1652] The device then displays the received feedback in the user's field of view, such as arrows or highlights to indicate correct finger placement, or textual explanations of pitch discrepancies, adjusting the display based on the user's emotional state.

[1653] Specific examples

[1654] Example 1: Piano practice

[1655] When the user begins to play the piano, the device's sensor module collects audio data (pitch, rhythm) and motion data (hand and finger movements), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1656] The device transmits this data to an analysis server in real time.

[1657] The analysis server analyzes the data and identifies any pitch deviations or improper finger positioning, as well as detecting when the user is slightly tense.

[1658] The analysis server generates an animation showing the correct finger movement, along with text such as "The C4 note is 0.5 seconds late. You need to press the key faster," and adds relaxing audio guidance.

[1659] The device displays this feedback in the user's field of view, allowing the user to correct their performance in real time.

[1660] Example 2: Violin practice

[1661] When the user begins to play the violin, the device's sensor module collects audio data (pitch, rhythm) and motion data (bow movement, left hand positioning), and the emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1662] The device transmits this data to an analysis server in real time.

[1663] The analysis server analyzes the data and identifies when the bow movement is not smooth or the pitch is off, as well as when the user is concentrating.

[1664] The analysis server generates text such as "The pitch of the E is off. You need to raise your finger position a little," along with guidelines for correcting the bow movement.

[1665] The device displays this feedback in the user's field of vision, allowing the user to practice to acquire correct playing technique.

[1666] Example prompts for generative AI models

[1667] An example of a prompt is:

[1668] This music education system collects real-time data on the user's performance, compares it with that of an expert, and provides suggestions for improvement. Please generate specific examples of feedback when the user begins to play the piano. Also, please include adjusting the feedback accordingly if the user is nervous.

[1669] By writing prompts in this way, it becomes possible to generate appropriate feedback using a generative AI model. This system allows users to improve their performance skills efficiently and practice while reducing psychological burden.

[1670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1671] Step 1: User starts playing

[1672] The system begins to operate when the user starts playing an instrument (e.g., piano or violin).

[1673] Input: User begins playing an instrument.

[1674] Output: Triggering data collection for the sensor module and emotion recognition camera.

[1675] Step 2: Device collects data

[1676] The device's sensor module collects audio and motion data: the audio sensor captures the pitch and rhythm of the instrument, and the motion sensor captures the movement and posture of the hands and fingers.

[1677] The emotion recognition camera analyzes the user's facial expressions to detect their emotional state.

[1678] Input: User performance data.

[1679] Output: Collected audio, motion, and emotion data.

[1680] Step 3: The device sends the data to the analysis server

[1681] The device sends the collected data to an analysis server in real time over a secure network.

[1682] Input: Collected voice, motion, and emotion data.

[1683] Output: Performance data and emotion data sent to the analysis server.

[1684] Step 4: The analysis server analyzes the data

[1685] The analysis server analyzes the transmitted data, extracting pitch and rhythm from the audio data, analyzing hand and finger movements and posture from the motion data, and identifying the user's emotional state based on data from the emotion recognition camera.

[1686] Input: Submitted voice, motion, and emotion data.

[1687] Output: Analysis of pitch, rhythm, hand and finger movements, posture, and emotional state.

[1688] Step 5: The analysis server converts the analysis results into feedback

[1689] Based on the analysis results, the analysis server compares the user's performance data with a database of expert performances, identifies discrepancies, and extracts specific areas for improvement.

[1690] Adjust the content and tone of your feedback based on the user's emotional state.

[1691] Input: Analysis results and expert data.

[1692] Output: Adjusted feedback data (improvements, advice).

[1693] Step 6: The analysis server sends feedback to the device

[1694] The analysis server transmits the generated feedback to the terminal.

[1695] The feedback includes visual guidelines, text, and audio prompts, and is adjusted based on the user's emotional state.

[1696] Input: Feedback data.

[1697] Output: Feedback sent to the device.

[1698] Step 7: The device displays feedback to the user

[1699] The device displays the received feedback in the user's field of view, which may include arrows or highlights indicating the correct finger positions, text explaining pitch discrepancies, and audio guidance.

[1700] The display is adjusted based on the user's emotional state, for example by using relaxing colors and audio prompts.

[1701] Input: Feedback sent to the device.

[1702] Output: Visual and auditory feedback provided to the user.

[1703] The above is the specific processing steps of the program of this system, its detailed operation, and the flow of input and output.

[1704] (Application example 2)

[1705] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1706] While conventional musical instrument performance training systems can provide feedback on a user's performance technique, they are unable to provide feedback that takes into account the user's emotional state. As a result, users are unable to receive appropriate feedback when they are in a tense or stressed situation, making it difficult to improve their performance technique efficiently.

[1707] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor module that collects the user's musical instrument performance in real time, means for analyzing the data collected from the sensor module and comparing it with a database of expert performances, means for visually and audibly presenting feedback from the analysis server to the user, an emotion recognition camera that analyzes the user's emotional state in real time, and a process in the analysis server that adjusts the content of the feedback based on the data collected from the emotion recognition camera. This enables appropriate feedback that takes into account both the user's performance technique and emotional state.

[1708] A "sensor module" is a device that collects audio data, motion data, and environmental data related to a user's musical instrument performance in real time.

[1709] The "analysis server" is a device that analyzes data collected from the sensor module and emotion recognition camera, compares it with a database of expert performances, and generates feedback.

[1710] A "terminal" is a device that provides visual and auditory feedback to a user from the analysis server.

[1711] An "emotion recognition camera" is a camera device that analyzes a user's facial expressions in real time and detects their emotional state.

[1712] The "expert performance database" is a database that accumulates performance data of experts with high skills in playing musical instruments.

[1713] "Feedback" refers to information including suggestions for improvement and advice regarding the user's musical instrument playing.

[1714] "Real-time" means that data collection, analysis, and feedback are instantaneous, with no delay.

[1715] "Audio data" refers to data that records the sounds produced by playing a musical instrument.

[1716] "Motion data" refers to data relating to the movements and posture of hands and fingers while playing an instrument.

[1717] "Environmental data" refers to data related to the performance environment, such as surrounding sounds and position information while playing an instrument.

[1718] "Feedback urgency" is a measure of the priority or importance of feedback.

[1719] "Feedback tone" refers to the way feedback is expressed and the tone of the language used.

[1720] This invention is a system for supporting musical instrument playing education, and is mainly composed of a sensor module, an analysis server, a terminal, and an emotion recognition camera. A specific embodiment of this system will be described below.

[1721] 1. System Configuration

[1722] User device (smart glasses)

[1723] The smart glasses worn by the user are equipped with built-in audio, motion, and environmental sensors, as well as an emotion-recognition camera. While the user plays an instrument, these sensors collect audio, motion, and environmental data in real time. The emotion-recognition camera also analyzes the user's facial expressions to detect their emotional state.

[1724] Sensor Module

[1725] Audio sensor: Captures the pitch and rhythm of musical instruments.

[1726] Motion sensor: Records hand and finger movements, posture, etc.

[1727] Environmental sensors: collect background noise, location information, etc.

[1728] Analysis Server

[1729] The analysis server analyzes the data sent from the smart glasses in real time. Its main functions are as follows:

[1730] Data analysis: Analyzes performance data such as pitch, rhythm, positioning, finger movement, etc. Identifies the user's emotional state based on data from the emotion recognition camera.

[1731] Database matching: Matching with a database of expert performances to identify discrepancies and errors in performance.

[1732] Feedback generation: Feedback is generated based on the analysis results, and the urgency and tone are adjusted taking into account the user's emotional state.

[1733] 2. Providing feedback

[1734] The generated feedback is displayed on the smart glasses' display. The feedback is provided in both visual and auditory forms. For example, a text message indicating pitch misalignment or an animation showing the correct finger movement is displayed. If the user is tense, audio prompts and visual effects are added to help them relax.

[1735] 3. Hardware and Software

[1736] Hardware: Smart glasses (voice sensor, motion sensor, environmental sensor, emotion recognition camera)

[1737] Software: Python, OpenCV (facial expression analysis), Librosa (voice analysis), Scikit-learn (emotion model)

[1738] 4. Specific Examples

[1739] Piano practice:

[1740] As the user plays the piano, sensors in the smart glasses collect voice and motion data, while an emotion-recognition camera analyzes the user's facial expressions.

[1741] The analysis server compares the data and generates text such as "The C4 note is 0.5 seconds late. You need to press the key faster," as well as an animation showing the correct finger movement.

[1742] If the user is nervous, voice guidance such as "Take a deep breath to relax" will also be added.

[1743] Violin Practice:

[1744] As the user plays the violin, data is collected and analyzed in a similar manner.

[1745] The analysis server generates feedback such as, "The E note is out of tune. You need to raise your finger position a little," and also provides guidelines for correcting bow movement.

[1746] Prompt Sentence Examples

[1747] Analyze the user's performance data to identify pitch and rhythm discrepancies. Generate appropriate feedback taking into account the user's emotional state. For example, if the user is playing behind the tempo, say, "You're behind. Please play a little faster." If the user is tense, add advice like, "Play more relaxed."

[1748] In this way, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[1749] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1750] Step 1:

[1751] When a user begins playing an instrument, the device's (smart glasses) sensor module collects audio data, motion data, and environmental data. Specifically, the audio sensor captures the instrument's pitch and rhythm, while the motion sensor records hand and finger movements and posture. The environmental sensor also collects ambient noise and location information. This data, along with data from the emotion recognition camera, is sent in real time to an analysis server.

[1752] Input: User's playing sounds, hand and finger movements, surrounding environment

[1753] Output: Audio data, motion data, environmental data, emotion data

[1754] Step 2:

[1755] The server uses Librosa to analyze the received audio data. Specifically, it extracts pitch and rhythm from the audio data and obtains tempo and pitch information. It then analyzes the motion and environmental data. It extracts hand and finger positioning from the motion data and identifies the performance environment from the environmental data.

[1756] Input: Audio data, motion data, environmental data

[1757] Output: Pitch, rhythm, positioning, and playing environment information

[1758] Step 3:

[1759] The server analyzes data from the emotion recognition camera in real time using OpenCV. Specifically, it detects the user's emotional state (e.g., joy, anger, sadness, surprise, etc.) from their facial expressions. The detected emotional information is used as part of the analysis process when generating feedback.

[1760] Input: facial expression data

[1761] Output: Emotional state (happy, angry, sad, surprised, etc.)

[1762] Step 4:

[1763] The server compares the extracted data with a database of expert performances. It identifies pitch discrepancies, rhythmic delays, and errors in hand and finger positioning, and compares the data with pre-stored expert data to highlight any discrepancies. This comparison process identifies specific areas for improvement in the user's performance.

[1764] Input: pitch, rhythm, positioning, expert performance database

[1765] Output: Performance improvements and differences

[1766] Step 5:

[1767] The server generates feedback to provide to the user based on the analysis data and the user's emotional state. The feedback includes specific advice and areas for improvement in the performance. The content of the feedback is also adjusted according to the user's emotional state. For example, if the user is nervous, advice to relax may be added.

[1768] Input: Improvements, Difference Information, Emotional State

[1769] Output: Feedback (improvements, advice)

[1770] Step 6:

[1771] The generated feedback is sent to the device and presented to the user visually and audibly. Visual feedback is displayed on the smart glasses in the form of text or animation, while auditory feedback is provided as audio guidance. For example, arrows or highlights indicating correct finger movements, text messages indicating pitch deviations, and audio guidance for relaxation are also provided.

[1772] Input: Feedback

[1773] Output: Visual feedback, auditory feedback

[1774] Through these steps, users can receive real-time feedback and efficiently improve their performance skills. Furthermore, feedback based on emotional state can reduce the psychological burden while playing.

[1775] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1776] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1777] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1778] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1779] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1780] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1781] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1782] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1783] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1784] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1785] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1786] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1787] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1788] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1789] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1790] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1791] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1792] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1793] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1794] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1795] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1796] The following is further disclosed regarding the above embodiment.

[1797] (Claim 1)

[1798] A sensor module that collects the user's musical instrument performance in real time;

[1799] an analysis server that analyzes the data collected from the sensor module and compares it with a database of expert performances;

[1800] a terminal that presents visual and auditory feedback to a user from the analysis server;

[1801] A system including:

[1802] (Claim 2)

[1803] 10. The system of claim 1, wherein the sensor module collects audio data, motion data, and environmental data.

[1804] (Claim 3)

[1805] The system according to claim 1, characterized in that the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data and compares it with a database of expert performances.

[1806] "Example 1"

[1807] (Claim 1)

[1808] a sensor module means for collecting user performance in real time;

[1809] an analysis server means for analyzing the data collected from the sensor module means and comparing it with a database of expert performances;

[1810] a terminal means for visually and audibly presenting feedback from the analysis server means to a user;

[1811] A system including:

[1812] (Claim 2)

[1813] 10. The system of claim 1, wherein said sensor module means collects audio data, motion data, and environmental data.

[1814] (Claim 3)

[1815] 2. The system according to claim 1, wherein the analysis server means analyzes pitch, rhythm, positioning, and finger movement based on the collected data and compares the results with a database of expert performances.

[1816] "Application Example 1"

[1817] New Claims:

[1818] (Claim 1)

[1819] A sensor module that collects the user's musical instrument performance in real time;

[1820] an analysis server that analyzes the data collected from the sensor module and compares it with a database of expert performances;

[1821] a terminal that presents visual and auditory feedback to a user from the analysis server;

[1822] A means for transmitting and receiving data through an application installed on a smartphone;

[1823] a means for accumulating user performance data and storing expert feedback as a history;

[1824] A way for users to manage their practice schedules and check their progress.

[1825] A system including:

[1826] (Claim 2)

[1827] 10. The system of claim 1, wherein the sensor module collects audio data, motion data, and environmental data.

[1828] (Claim 3)

[1829] The system according to claim 1, characterized in that the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data and compares it with a database of expert performances.

[1830] "Example 2: Combining Emotion Engines"

[1831] (Claim 1)

[1832] a sensor module means for collecting the user's musical instrument performance in real time;

[1833] an analysis server means for analyzing the data collected from the sensor module and the emotion data collected from the emotion recognition camera and comparing the data with a database of expert performances;

[1834] a terminal means for visually and audibly presenting feedback from the analysis server to the user and adjusting the content and tone of the feedback based on the user's emotional state;

[1835] A system including:

[1836] (Claim 2)

[1837] 10. The system of claim 1, wherein the sensor module collects audio data, motion data, and environmental data.

[1838] (Claim 3)

[1839] The system described in claim 1, characterized in that the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data, and further analyzes emotional data to identify the user's emotional state and compares it with a database of expert performances.

[1840] "Application example 2 when combining emotion engines"

[1841] (Claim 1)

[1842] A sensor module that collects the user's musical instrument performance in real time;

[1843] an analysis server that analyzes the data collected from the sensor module and compares it with a database of expert performances;

[1844] a terminal that presents visual and auditory feedback to a user from the analysis server;

[1845] An emotion recognition camera that analyzes the user's emotional state in real time,

[1846] an analysis server process that adjusts the feedback content based on the data collected from the emotion recognition camera;

[1847] A system including:

[1848] (Claim 2)

[1849] 10. The system of claim 1, wherein the sensor module collects audio data, motion data, and environmental data.

[1850] (Claim 3)

[1851] The system described in claim 1, characterized in that the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data, compares it with a database of expert performances, and further adjusts the urgency and tone of feedback based on the user's emotional state. [Explanation of symbols]

[1852] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor module that collects the user's musical instrument performance in real time; an analysis server that analyzes the data collected from the sensor module and compares it with a database of expert performances; a terminal that presents visual and auditory feedback to a user from the analysis server; A system including:

2. The system of claim 1 , wherein the sensor module collects audio data, motion data, and environmental data.

3. 2. The system according to claim 1, wherein the analysis server analyzes pitch, rhythm, positioning, and finger movement based on the collected data and compares the results with a database of expert performances.

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