System

Abstract

A system is provided.SOLUTION: The system includes means for recording voice data of a user, means for transmitting the recorded voice data to a server, means for analyzing the voice data and evaluating singing skills of the user, means for generating a practice piece suitable for the user based on an evaluation result, and means for providing the generated practice piece 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] The present invention relates to a system that provides personalized practice songs for people who lack confidence in their singing skills or who want to practice singing effectively. Conventional singing practice methods have the drawback of making it difficult for users to find practice songs that suit their singing skills, and those who are reluctant to take face-to-face lessons have limited practice opportunities. To address these issues, there is a need for a system that accurately evaluates a user's singing skills and generates practice songs based on the evaluation. [Means for solving the problem]

[0005] The system of the present invention includes the following means: means for recording a user's voice data, means for transmitting the recorded voice data to a server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice pieces suitable for the user based on the evaluation results, and means for providing the generated practice pieces to the user. The voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, and tempo, and the practice piece generation means includes means for setting parameters such as tempo, range, and difficulty based on the user's singing skill.

[0006] This allows users to efficiently practice singing using practice songs that are suited to their skill level, and improve their singing skills.

[0007] A "user" is a person who uses this system to practice singing.

[0008] "Audio Data" means a recording of a user's singing voice, stored and transmitted in digital or analog form.

[0009] "Recording means" refers to a device or program for recording the voice sung by the user.

[0010] The "server" is a central system that receives and analyzes audio data sent by users and generates practice pieces.

[0011] "Audio analysis means" refers to algorithms or software that analyzes received audio data and evaluates the user's singing skill.

[0012] "Singing skill" refers to the user's technical singing ability, such as pitch, rhythm, range, and tempo.

[0013] The "practice piece generation means" is an algorithm or program for creating practice pieces suitable for a user based on an evaluation of the user's singing skill.

[0014] "Parameters" are elements that are taken into consideration when generating a practice piece, and include pitch, rhythm, range, tempo, difficulty level, and the like.

[0015] The "providing means" is a device or program for distributing the created practice piece to the user. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system that generates and provides personalized practice songs tailored to a user's singing skills. This system includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0038] System Overview

[0039] This system first records the user's voice data and sends the recording to a server. The server analyzes the received voice data to evaluate the user's singing skill and generates practice songs based on the evaluation results. The generated practice songs are then provided to the user, allowing them to practice singing efficiently.

[0040] Collecting user voice data

[0041] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[0042] Sending audio data

[0043] The device temporarily stores the recorded audio data and then transmits it to a server via the Internet.

[0044] Analysis of audio data

[0045] The server inputs the received audio data into a voice analysis engine, which analyzes the user's pitch, rhythm, range, tempo, and other parameters in detail to quantify their singing skills, making it clear which areas they are good at and which areas need improvement.

[0046] Etude generation

[0047] Based on the analysis results, the server generates practice songs suited to the user, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty level, and automatically creates songs that encourage the user's growth. For example, songs with minimal fluctuation in scale to stabilize pitch, or songs with many high-pitched parts to strengthen the high range, may be generated.

[0048] Providing practice pieces

[0049] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[0050] Specific examples

[0051] Example 1: Novice user

[0052] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user sings.

[0053] 2. The device will start recording and stop recording when you finish singing.

[0054] 3. The device sends the recorded data to the server.

[0055] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good.

[0056] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0057] 6. The server sends the generated practice piece to the device.

[0058] 7. The user practices using the provided practice songs.

[0059] Example 2: Intermediate user

[0060] 1. The device prompts the user to sing "Let it Go," and the user sings.

[0061] 2. The device will start recording and stop recording when you finish singing.

[0062] 3. The device sends the recorded data to the server.

[0063] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable.

[0064] 5. The server generates an etude with repeated high notes.

[0065] 6. The server sends the generated practice piece to the device.

[0066] 7. The user practices using the provided practice songs.

[0067] This allows users to effectively practice according to their singing ability, and can expect to improve their skills in a short period of time.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[0071] Step 2:

[0072] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[0073] Step 3:

[0074] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[0075] Step 4:

[0076] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[0077] Step 5:

[0078] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[0079] Step 6:

[0080] The server inputs the received audio data into a voice analysis engine, which performs detailed analysis of pitch, rhythm, range, and tempo.

[0081] Step 7:

[0082] The server evaluates the user's singing skills based on the analysis results, and the evaluation is quantified and saved in the user's skill profile.

[0083] Step 8:

[0084] The server sets parameters for generating practice songs based on the user's singing skill evaluation, including tempo, note range, and difficulty level.

[0085] Step 9:

[0086] The server creates personalized etudes using an etude generation algorithm that constructs the etude according to set parameters.

[0087] Step 10:

[0088] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[0089] Step 11:

[0090] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[0091] Step 12:

[0092] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[0093] Step 13:

[0094] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[0095] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills.

[0096] Example 1

[0097] 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."

[0098] Conventional singing practice systems have difficulty in providing detailed evaluations of individual users' singing skills and providing appropriate practice songs based on those evaluations, limiting the effectiveness of practice. Furthermore, despite technological advances in audio data analysis and practice song generation, there has been no system that effectively combines these technologies. As a result, users have been unable to efficiently and effectively improve their singing skills.

[0099] 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.

[0100] In this invention, the server includes means for recording the user's voice data, means for transmitting the recorded voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice songs suitable for the user based on the evaluation results, means for providing the generated practice songs to the user, means for analyzing the user's voice data based on elements such as pitch, rhythm, range, and tempo and quantifying the evaluation results, and means for using a generative AI model based on the analysis results to create practice songs that are optimal for the user based on prompt sentences, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. This allows users to practice individually and optimally based on their own singing skill, enabling them to effectively improve their skills in a short period of time.

[0101] A "user" is an individual who uses a recording means to provide audio data, receives practice songs, and practices singing.

[0102] A "terminal" is a device used by a user that records audio, transmits audio data to a server, and plays back practice pieces.

[0103] A "server" is a computing device or system that receives and analyzes audio data, and generates and provides optimal practice pieces to users.

[0104] The "recording means" is a function or device for capturing the user's singing voice and saving it as audio data.

[0105] "Transmission means" refers to a function or device for transmitting recorded audio data to a server via the Internet or other communication means.

[0106] The "analysis means" is a function or system that analyzes the received audio data, evaluates elements such as pitch, rhythm, range, and tempo, and quantifies the user's singing skill.

[0107] The "practice piece generation means" is a function or system that generates a practice piece suitable for the user based on the analysis results.

[0108] The "providing means" is a function or device that transmits the created practice piece to the user's terminal and enables the user to play the practice piece.

[0109] "Pitch" is an element that indicates the pitch of each note while the user is singing.

[0110] "Rhythm" is an element that identifies time elements such as the length of notes and beats while the user is singing.

[0111] "Range" is an element that indicates the range of sounds that a user can produce, from the lowest to the highest note.

[0112] "Tempo" is an element that indicates the speed and slowness of the sound being sung.

[0113] The "analysis results" are data that express the evaluation of pitch, rhythm, range, tempo, etc. obtained by the audio data analysis means in numerical or other formats.

[0114] A "generative AI model" is an artificial intelligence algorithm used to generate optimal practice songs that take into account the user's singing skills.

[0115] A "prompt" is an instruction given to a generative AI model, a document that specifies the characteristics and elements of the practice piece to be generated.

[0116] This clearly defines the function and role of each element included in the claims.

[0117] This invention relates to a system that generates and provides practice songs personalized to a user's singing skill. The system mainly includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means. These means work together to provide the user with the practice songs that are most suitable for them. An embodiment of the system is described in detail below.

[0118] Collecting user voice data

[0119] The device has a function for recording the user's singing voice. Specifically, the device is equipped with a microphone function, which the application uses to capture voice data. When the user sings the specified song according to the application's instructions, the voice is recorded.

[0120] example:

[0121] The device will display the instruction "Sing 'Twinkle Twinkle Little Star'" and begin recording.

[0122] The user starts singing and when they finish singing, the recording stops.

[0123] Sending audio data

[0124] The device temporarily stores the recorded audio data and then transmits it to a server. The transmission is via the Internet and the audio data is sent in compressed form. During transmission, checks are performed to ensure the data is intact.

[0125] example:

[0126] The device will display "Recording complete, sending data."

[0127] The terminal compresses the audio data and sends it to the server.

[0128] The server receives the data and returns a confirmation message.

[0129] Analysis of audio data

[0130] The server inputs the received voice data into a voice analysis engine. This engine utilizes a generative AI model to perform detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies the user's singing skill. The results of these analyses are then stored in a database.

[0131] example:

[0132] The server generates a log stating "Analyzing audio data."

[0133] The server divides the audio data into pitch and rhythm and analyzes it.

[0134] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[0135] Etude generation

[0136] Based on the analysis results, the server generates the most suitable practice songs for the user. In this case, a generative AI model is used to automatically create practice songs to improve the user's singing skills, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. The AI ​​model is given appropriate instructions (prompts).

[0137] example:

[0138] The server generates a log saying "Generating appropriate practice pieces."

[0139] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[0140] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[0141] Providing practice pieces

[0142] The generated practice songs are sent from the server to the user's device, which provides the user with an interface that can play the practice songs, allowing the user to listen to the practice songs and practice singing.

[0143] example:

[0144] The server generates a log stating "Sending practice songs to the device."

[0145] The device will display "New practice song received. Would you like to play it?"

[0146] The user selects play and the device plays the practice piece.

[0147] Example prompt

[0148] Below is an example of a prompt sentence that is input to the generative AI model.

[0149] For novice users:

[0150] "The user sang 'Twinkle Twinkle Little Star' and the audio data was sent to the server. Audio analysis revealed that the pitch was unstable but the rhythm was good. Please generate the best practice song for this user."

[0151] For intermediate users:

[0152] "The user sang 'Let it Go' and the audio data was sent to the server. Audio analysis showed that the pitch, rhythm, and tempo were mostly accurate, but the high notes were a little unstable. Please generate an optimal practice song for this user."

[0153] This allows users to effectively practice according to their singing skills, improving their singing skills in a short period of time.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The user follows the application's instructions to sing the specified song, and the device records the audio.

[0157] Specific behavior:

[0158] The device displays the instruction, "Sing 'Twinkle Twinkle Little Star'."

[0159] The user starts singing, and the device uses a microphone to capture the sound and generate audio data.

[0160] Input: User's singing voice

[0161] Output: Recorded audio data

[0162] Step 2:

[0163] The device temporarily stores the recorded audio data and then transmits it over the internet to a server, where checks are performed to ensure data integrity during transmission.

[0164] Specific behavior:

[0165] The device will display "Recording complete, sending data."

[0166] The terminal compresses the audio data and sends it to the server.

[0167] The server receives the data and returns a confirmation message.

[0168] Input: Recorded audio data

[0169] Output: Confirmation message, notification of completion of sending

[0170] Step 3:

[0171] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to perform detailed analysis of pitch, rhythm, range, tempo, and other elements to quantify the user's singing skill.

[0172] Specific behavior:

[0173] The server generates a log stating "Analyzing audio data."

[0174] The server analyzes the audio data, dividing it into pitch and rhythm for analysis.

[0175] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[0176] Input: Received audio data

[0177] Output: Voice analysis results, quantified skill level

[0178] Step 4:

[0179] The server generates practice songs suitable for the user based on the results of audio analysis, using a generative AI model to create the optimal practice songs, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty.

[0180] Specific behavior:

[0181] The server generates a log saying "Generating appropriate practice pieces."

[0182] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[0183] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[0184] Input: Voice analysis results, prompt text

[0185] Output: Generated exercises

[0186] Step 5:

[0187] The server sends the created practice piece to the terminal, which provides the user with an interface that can play the practice piece, allowing the user to listen to the practice piece and practice singing.

[0188] Specific behavior:

[0189] The server generates a log stating "Sending practice songs to the device."

[0190] The device will display "New practice song received. Would you like to play it?"

[0191] The user selects play and the device plays the practice piece.

[0192] Input: Generated Etude

[0193] Output: Practice song to play, user starts singing practice

[0194] By clarifying the specific operations performed at each step and the input / output flow in this way, it becomes easier to understand the processing flow of the entire system and the role of each step.

[0195] (Application example 1)

[0196] 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."

[0197] In recent years, the importance of singing practice tailored to individual needs has increased in the field of music education. However, conventional singing practice systems often lack the functionality to automatically generate and provide appropriate practice songs based on the user's singing skill. They also lack a mechanism to visualize the user's progress and provide specific feedback. This has made it difficult to provide efficient and effective practice.

[0198] 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.

[0199] In this invention, the server includes means for recording the user's voice data, means for transmitting the voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice pieces suitable for the user based on the evaluation results, means for providing the generated practice pieces to the user, means for managing the progress of the user's singing skill based on the analysis results and visualizing the progress, and means for providing specific advice based on the practice results. This not only makes it possible to provide practice pieces that meet the individual needs of the user, but also makes it possible to visualize the user's progress and provide objective and specific feedback.

[0200] "User" refers to an individual who uses this system to practice singing.

[0201] "Audio data" refers to data containing information about audio signals recorded when a user sings.

[0202] "Server" refers to a data center or computing infrastructure for receiving and analyzing audio data, and generating and providing practice pieces.

[0203] "Recording means" means any device or software used to digitally record a user's singing voice.

[0204] "Transmitting means" refers to a network communication means for transferring recorded audio data to a server.

[0205] "Means of analysis" refers to algorithms and software that use an audio analysis engine to perform detailed analysis of pitch, rhythm, range, tempo, etc.

[0206] "Evaluation" refers to quantifying a user's singing skills based on the analysis results.

[0207] "Means for generating" refers to a program or algorithm that automatically creates practice pieces suitable for the user based on the analysis results.

[0208] "Means of providing" refers to the mechanism for sending the generated practice piece to the user's device and making it playable.

[0209] "Progress management tools" refers to systems and features that record and visualize the user's progress in singing skills.

[0210] "Specific advice" refers to feedback such as improvements and recommendations provided to users based on their practice results.

[0211] The present invention relates to a system for generating and providing practice songs personalized to a user's singing skills, including a recording means, a transmitting means, a analyzing means, a evaluating means, a generating means, a providing means, a progress management means, and a means for providing specific advice.

[0212] System Overview

[0213] The entire system starts by recording the user's voice data and sending it to the server. The server receives the voice data and analyzes it using a voice analysis engine. Based on the analysis results, the server evaluates the user's singing skills and generates personalized practice songs according to the evaluation results. The practice songs are then provided to the user's device, which further visualizes the user's progress and provides specific advice.

[0214] Recording audio data

[0215] Users record their singing using a device (smartphone, smart glasses, head-mounted display, etc.), and the recording data is digitally recorded and temporarily stored on the device.

[0216] Sending audio data

[0217] The recorded audio data is transmitted to a server via the Internet using an appropriate network communication means.

[0218] Analysis of audio data

[0219] The server analyzes the received audio data and uses an audio analysis engine (such as librosa or a similar audio analysis library) to perform detailed analysis of the user's pitch, rhythm, range, tempo, and other characteristics, converting them into numerical values.

[0220] Singing skill evaluation

[0221] Based on the analysis, the server evaluates the user's singing skills based on a number of parameters, including pitch stability, rhythmic accuracy, range, and tempo consistency.

[0222] Etude generation

[0223] Based on the evaluation results, the server generates an optimal practice piece for the user, with parameters such as tempo, note range, and difficulty set according to the user's skill level.

[0224] Providing practice pieces

[0225] The generated practice piece is sent from the server to the user's device, which then provides the received practice piece to the user in a playable format.

[0226] Progress management and advice provision

[0227] The server records the user's practice history, visualizes their progress, and provides specific advice based on their practice results, such as "Practice your high-pitched notes more."

[0228] Specific examples

[0229] For example, if a user sings "Twinkle Twinkle Little Star" on their smartphone, the recording is sent to the server for analysis. If the analysis determines that the user's pitch is unstable, a practice song with a slower tempo and less variation in the scale is generated and provided to the user. The user can then practice with the provided practice song to improve their skills.

[0230] Prompt Sentence Examples

[0231] example:

[0232] Analyze the user's audio data (example.wav) and evaluate its tempo, pitch, rhythm, etc. Then, based on the results of this evaluation, generate an optimal practice piece for the user. The generated practice piece should be designed as a simple piece for beginners, with a slow tempo and minimal variation in the scale.

[0233] This example allows the system to provide specific and effective personalized practice songs to help users improve their singing skills.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] The user uses the device to record their singing voice. Specifically, they launch the recording application and start singing a designated song, and the audio data is recorded in digital format through the device's built-in microphone. The input is the user's singing voice, and the output is digital audio data.

[0237] Step 2:

[0238] The device temporarily stores the recorded audio data and transmits it to a server via the Internet using a data transfer protocol (e.g. HTTP / HTTPS). The input is digital audio data, and the output is data transmission to the server.

[0239] Step 3:

[0240] The server analyzes the received audio data. Using an audio analysis engine (e.g., librosa library), it performs detailed analysis of characteristics such as pitch, rhythm, range, and tempo. The input is digital audio data, and the output is numerical data containing the analysis results.

[0241] Step 4:

[0242] The server evaluates the user's singing skill based on the analysis results, using parameters such as pitch stability, rhythm accuracy, range, and tempo consistency. The input is numerical data containing the analysis results, and the output is evaluation data that evaluates the user's singing skill.

[0243] Step 5:

[0244] The server generates the most suitable practice pieces for the user based on the evaluation results. It uses a generative AI model to automatically create practice pieces that fit the evaluation data. The input is the evaluation data, and the output is personalized practice piece data.

[0245] Step 6:

[0246] The server then sends the generated exercise to the user's device, converting the exercise data into an appropriate format (e.g., MP3 file) and transmitting it over the Internet. The input is the personalized exercise data, and the output is the transmission of the exercise to the user's device.

[0247] Step 7:

[0248] The user's device plays the received practice songs, and the user practices singing using them. Specifically, the media player function for playing practice songs is used. The input is the practice song data, and the output is audio playback.

[0249] Step 8:

[0250] The server records the user's practice history and stores and analyzes the data to visualize their progress. This allows them to compare their progress with past practice data. The input is the user's practice history data, and the output is the visualized progress data.

[0251] Step 9:

[0252] The server provides specific advice to the user based on the practice results. For example, it generates feedback such as "Practice more on the high-pitched parts" based on the analysis results and sends it to the user. The input is the practice result data, and the output is a specific advice message.

[0253] 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.

[0254] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0255] System Overview

[0256] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[0257] Collecting user voice data

[0258] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[0259] Sending audio data

[0260] The device temporarily stores the recorded audio data in its storage, and then transmits the data to a server via the Internet.

[0261] Analysis of audio data

[0262] The server inputs the received voice data into a voice analysis engine. This engine performs detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies their singing skills. This makes it clear which areas they are good at and which areas need improvement. The emotion engine also recognizes the user's emotions from the voice data and quantifies them.

[0263] Etude generation

[0264] The server generates practice songs suited to the user based on the results of voice analysis and emotion recognition. It automatically creates songs that encourage the user's growth by taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotions. For example, if the user seems sad, it will generate a song with a slow tempo, and if they seem excited, it will generate a song with a fast tempo.

[0265] Providing practice pieces

[0266] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[0267] Specific examples

[0268] Example 1: Novice user

[0269] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[0270] 2. The device will start recording and stop recording when you finish singing.

[0271] 3. The device sends the recorded data to the server.

[0272] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion engine also determines that the user is relaxed.

[0273] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0274] 6. The server sends the generated practice piece to the device.

[0275] 7. The user practices using the provided practice songs.

[0276] Example 2: Intermediate user

[0277] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[0278] 2. The device will start recording and stop recording when you finish singing.

[0279] 3. The device sends the recorded data to the server.

[0280] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. The emotion engine also determines that the user is excited.

[0281] 5. The server generates a fast-paced etude with repeated high notes.

[0282] 6. The server sends the generated practice piece to the device.

[0283] 7. The user practices using the provided practice songs.

[0284] This allows users to effectively practice according to their singing skills and emotional state.

[0285] The processing flow will be explained below.

[0286] Step 1:

[0287] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[0288] Step 2:

[0289] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[0290] Step 3:

[0291] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[0292] Step 4:

[0293] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[0294] Step 5:

[0295] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[0296] Step 6:

[0297] The server inputs the received audio data into a voice analysis engine, which analyzes the pitch, rhythm, range, and tempo in detail and generates numerical evaluation data.

[0298] Step 7:

[0299] The server also inputs the voice data into the emotion engine, which recognizes the user's emotion based on features extracted from the voice and generates emotion evaluation data.

[0300] Step 8:

[0301] The server combines data from both the voice analysis engine and the emotion engine to evaluate the user's singing skill and emotional state, and stores the results of these evaluations as a numerical value in the user profile.

[0302] Step 9:

[0303] Based on the evaluation results, the server sets parameters to generate practice pieces suited to the user, including tempo, range of notes, difficulty level, and emotional state.

[0304] Step 10:

[0305] The server uses a practice song generation algorithm to create practice songs according to the set parameters, such as songs for stabilizing pitch or songs with tempo adjustments based on the user's emotional state.

[0306] Step 11:

[0307] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[0308] Step 12:

[0309] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[0310] Step 13:

[0311] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[0312] Step 14:

[0313] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[0314] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills and emotions.

[0315] Example 2

[0316] 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."

[0317] Conventional singing practice systems have been unable to provide practice songs that take into account not only the user's singing skill but also their emotional and psychological state. As a result, users are unable to practice effectively to improve their singing skills, and it is difficult to provide a practice experience that suits each individual's emotional state. There is also a need for a system that can clearly identify the user's specific weaknesses and challenges and generate practice songs that are suited to them.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0319] In this invention, the server includes means for analyzing the voice data and evaluating the user's singing skill and emotions, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece to the user, thereby enabling the provision of practice pieces that take into account both the user's singing skill and emotional state.

[0320] "User's voice data" refers to voice information sung by a user, and is digital data collected by a recording device.

[0321] "Recording means" refers to devices or software that record audio as digital data.

[0322] "Electronic computer" refers to a computer or server that has the ability to receive, process, store, and analyze voice data.

[0323] "Means for analyzing" refers to software or algorithms for analyzing digitized audio data and extracting specific parameters.

[0324] "Singing skill" is a numerical representation of a user's musical ability, such as pitch, rhythm, range, and tempo.

[0325] "Emotion" refers to the psychological state and mood that the user has when singing, and is information analyzed from audio data.

[0326] "Means of evaluation" refers to the process of quantifying a user's singing skills and emotions based on analyzed audio data.

[0327] "Means for generating practice songs" refers to algorithms or software that automatically generate new practice songs based on the user's singing skill and emotions.

[0328] "Means provided to the user" refers to an interface or application that allows the user to play the generated practice piece.

[0329] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0330] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[0331] Collecting user voice data

[0332] The device uses a built-in or externally connected microphone to record the user's singing voice. When the user sings the designated song following the system's instructions, the voice is recorded and saved as high-quality digital data.

[0333] Sending audio data

[0334] The device reads the recorded audio data from the storage and transmits it to the server over the Internet, using secure protocols such as SSL / TLS to protect the confidentiality and integrity of the data.

[0335] Analysis of audio data

[0336] The server inputs the received voice data into an analysis engine (e.g., a general voice analysis engine). This engine performs detailed analysis of the user's skill elements, such as pitch, rhythm, range, and tempo, and generates a skill score. For emotion analysis, a separate emotion engine (e.g., a general emotion analysis engine) is used to recognize the user's emotions and quantify the results.

[0337] Etude generation

[0338] The server then uses these analysis results to run a music generation algorithm (for example, an algorithm built using a Python music generation library) to generate practice pieces. The algorithm creates pieces based on parameters such as pitch, rhythm, range, tempo, and difficulty. It also takes into account the user's emotional state; for example, if the user is sad, it generates a slower song, and if the user is excited, it generates a faster song.

[0339] Providing practice pieces

[0340] The generated practice songs are sent from the server to the terminal, and the terminal provides the user with an interface for playing the received practice songs, allowing the user to practice singing using the provided practice songs.

[0341] Specific examples

[0342] Example 1: Novice user

[0343] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[0344] 2. The device will start recording and stop recording when you finish singing.

[0345] 3. The device sends the recorded data to the server.

[0346] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. Emotion analysis also determines that the user is relaxed.

[0347] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0348] 6. The server sends the generated practice piece to the device.

[0349] 7. The user practices using the provided practice songs.

[0350] Example 2: Intermediate user

[0351] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[0352] 2. The device will start recording and stop recording when you finish singing.

[0353] 3. The device sends the recorded data to the server.

[0354] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. Emotion analysis also determines that the user is excited.

[0355] 5. The server generates a fast-paced etude with repeated high notes.

[0356] 6. The server sends the generated practice piece to the device.

[0357] 7. The user practices using the provided practice songs.

[0358] This system allows users to effectively practice according to their singing skills and emotional state.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] The user logs into the system and selects a song to sing.

[0362] Input: Username and password, list of practice songs

[0363] Data processing: User authentication (database matching), acquisition of practice song list

[0364] Output: Authentication success message, practice song list

[0365] Specific operation: The user logs in by entering their username and password on the device. If login is successful, the device retrieves a list of practice songs from the server and displays it to the user. The user then selects the song they want to practice from the displayed list.

[0366] Step 2:

[0367] The device records the user's voice.

[0368] Input: User's singing voice

[0369] Data processing: Digital conversion of audio, high-quality recording, noise canceling processing

[0370] Output: Recorded audio data

[0371] How it works: When a user starts singing along with a selected song, the device will record the sound using a built-in or externally connected microphone. This recording will then be temporarily stored as digital data in storage.

[0372] Step 3:

[0373] The device sends the recorded data to the server.

[0374] Input: Pre-recorded audio data

[0375] Data processing: Reading audio data and preparing it for transmission, encryption via SSL / TLS

[0376] Output: Notification of completion of transmission to the server

[0377] Specific operation: The recorded audio data is read from the storage and sent to the server via the Internet. When sending the data, confidentiality is maintained using secure protocols such as SSL / TLS.

[0378] Step 4:

[0379] The server analyzes the audio data and evaluates singing skill and emotion.

[0380] Input: Audio data

[0381] Data processing: Analysis of pitch, rhythm, range, tempo, emotion recognition and quantification

[0382] Output: Singing skill score and emotion score

[0383] Specific operation: After receiving the voice data, the server inputs the data into a voice analysis engine (e.g., a general voice analysis engine) to perform a detailed analysis of the user's singing skills. After that, it uses an emotion analysis engine to recognize the user's emotional state and quantify each of them.

[0384] Step 5:

[0385] The server generates practice pieces based on the analysis results.

[0386] Input: Singing skill score and emotion score

[0387] Data processing: Execution of music generation algorithms, automatic generation of practice songs

[0388] Output: Generated practice piece data

[0389] How it works: Based on the analysis results, the server runs a music generation algorithm using Python to automatically generate practice pieces optimized for the user's skill and emotions. Parameters such as tempo, range, and difficulty are adjusted to generate songs that also take the user's emotional state into account.

[0390] Step 6:

[0391] The server sends the generated practice piece to the terminal.

[0392] Input: Generated etude data

[0393] Data processing: Reading and sending practice song data, encryption by SSL / TLS

[0394] Output: Notification of completion of transmission to the terminal

[0395] Specific operation: The generated practice piece is temporarily stored and then sent to the device via the Internet. When sending data, confidentiality is maintained using secure protocols such as SSL / TLS.

[0396] Step 7:

[0397] The device plays the practice songs and provides them to the user.

[0398] Input: Received practice piece data

[0399] Data processing: Preparation for playback, display of playback interface

[0400] Output: Practice piece played, user feedback

[0401] Specific operation: The device provides an interface for playing the received practice songs, and the user can practice singing while playing the songs on the device. The interface also has loop playback and speed adjustment functions to support the user's practice experience.

[0402] (Application example 2)

[0403] 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."

[0404] Existing singing practice systems focus only on the user's singing skills and do not take into account their emotional state. This makes it difficult for users to practice in a way that suits their emotions. Furthermore, the practice songs provided to users are not individually customized, making it difficult for users to maintain their growth and motivation. Furthermore, the lack of effective feedback makes it difficult for users to understand where they need to improve.

[0405] The specific processing by the specific 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 means for analyzing the user's voice data and evaluating the singing skill and emotion, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece using a prompt sentence. This allows the user to use a practice piece individually customized according to their singing skill and emotional state, enabling effective practice that helps them maintain motivation.

[0406] A "user" is a person who uses this system to practice singing.

[0407] "Audio data" refers to audio information recorded when a user sings.

[0408] A "server" is a computer system that receives audio data, analyzes it, and generates practice pieces.

[0409] "Evaluation" refers to the use of analytical means to quantify and judge the user's singing skill and emotional state.

[0410] "Etudes" are pieces of music generated according to the user's singing skills and emotions.

[0411] The "analysis means" is a system that has the function of analyzing and quantifying the user's singing skills and emotions from audio data.

[0412] "Emotion" refers to the psychological state of the user while singing, and is quantified by analytical means.

[0413] "Providing" means sending the generated practice piece to the user's device so that the user can practice it.

[0414] A "prompt" is a sentence that instructs the user on the next action or exercise to be taken.

[0415] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. Detailed embodiments of this system will be described below.

[0416] System Overview

[0417] This system records the user's voice data and sends the recorded data to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing effectively.

[0418] Hardware and Software

[0419] Terminal: A device that records and plays audio data and communicates with a server. Examples include smartphones, tablets, and head-mounted displays.

[0420] Server: A computer system that analyzes and evaluates audio data and generates practice pieces. The main software used is a voice analysis engine (e.g., librosa), an emotion recognition engine (e.g., EmotionRecognition), and a practice piece generation engine (e.g., generative AI model).

[0421] Communication method: An internet connection to transfer data between your device and our server.

[0422] Recording and transmitting audio data

[0423] The user uses the device to sing a designated song according to a specific guide, and the device records this voice and transmits the recorded data to a server via the Internet.

[0424] Analysis and evaluation of speech data

[0425] The server inputs the received voice data into a voice analysis engine, which analyzes and quantifies the user's pitch, rhythm, range, and tempo in detail. It also uses an emotion recognition engine to analyze the user's emotional state from the voice data and quantifies this as well.

[0426] Etude generation

[0427] Based on the results of voice analysis and emotion recognition, the server generates practice songs suited to the user. The song is automatically created taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotional state. This allows the server to provide a fast-tempo song if the user is excited, and a slower-tempo song if the user is relaxed.

[0428] Providing practice pieces

[0429] The generated practice pieces are sent from the server to the user's device, which provides an interface for playing the received practice pieces to support the practice. For playback, a library such as Pydub is used.

[0430] Specific use cases

[0431] For novice users

[0432] 1. The device prompts the user to sing a "simple song."

[0433] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[0434] 3. The device sends the recorded data to the server.

[0435] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion recognition engine also determines that the user is relaxed.

[0436] 5. The server generates the appropriate practice piece and sends it to the user's device.

[0437] 6. The user practices using the practice songs provided.

[0438] For intermediate users

[0439] 1. The device prompts the user to sing a "medium difficulty song."

[0440] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[0441] 3. The device sends the recorded data to the server.

[0442] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are unstable. The emotion recognition engine also determines that the user is excited.

[0443] 5. The server generates a fast-paced practice piece with repeated high notes and sends it to the user's device.

[0444] 6. The user practices using the practice songs provided.

[0445] Prompt Sentence Examples

[0446] Design a system that analyzes a user's singing audio data and generates personalized practice songs. Use the following information:

[0447] Records audio data and sends it to the server

[0448] Server-side voice analysis evaluates the user's singing skill and emotional state

[0449] Generates customized practice pieces based on user analysis results

[0450] Provides practice pieces and displays a guiding interface for users

[0451] Examples:

[0452] 1. Beginner users should sing "easy songs."

[0453] 2. Intermediate users sing "songs of medium difficulty."

[0454] In this way, the system can provide effective singing practice according to the user's singing skill and emotions.

[0455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0456] Step 1:

[0457] The user begins singing.

[0458] The device displays a guide message to the user and instructs them to sing a specified song. The input is the user's real-time voice, and the output is a trigger to start recording.

[0459] Step 2:

[0460] The device records the user's singing voice.

[0461] When the user starts singing, the device records the voice data using the built-in microphone. The input is the user's singing voice (analog signal), and the output is the recorded digital voice file (e.g., WAV format).

[0462] Step 3:

[0463] Once the recording is complete, the device sends the audio data to the server.

[0464] When the user finishes singing, the device temporarily stores the audio data and sends it to a server via the Internet. The input is a digital audio file, and the output is a status indicating that data transfer to the server has been completed.

[0465] Step 4:

[0466] The server analyzes the received audio data.

[0467] The server inputs the received audio data into an analysis engine (e.g., librosa), which analyzes and quantifies the pitch, rhythm, range, and tempo in detail. The input is a digital audio file, and the output is quantified singing skill data.

[0468] Step 5:

[0469] The server evaluates the emotion using an emotion recognition engine.

[0470] The server inputs the analyzed voice data into an emotion recognition engine (e.g., EmotionRecognition), which analyzes and quantifies the user's emotional state. The input is quantified singing skill data, and the output is quantified emotional data.

[0471] Step 6:

[0472] The server generates practice songs based on singing skill and emotional state.

[0473] The server provides singing skill data and emotional data as input to the generative AI model, which then generates the optimal practice song for the user. The input is singing skill data and emotional data, and the output is a digital file of the generated practice song.

[0474] Step 7:

[0475] The server transmits the generated practice piece to the terminal.

[0476] The server sends the generated practice piece to the user's device. The input is the digital file of the created practice piece, and the output is the status of the completion of data transfer to the device.

[0477] Step 8:

[0478] The device provides the practice songs to the user and plays them.

[0479] The device displays an interface for playing the received practice piece and prompts the user to practice. The input is the generated digital file of the practice piece, and the output is the user's practice behavior.

[0480] Through the above steps, the user can practice singing effectively using practice songs that are individually customized according to the user's singing skill and emotional state.

[0481] 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.

[0482] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0483] 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.

[0484] [Second embodiment]

[0485] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0486] 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.

[0487] 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).

[0488] 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.

[0489] 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.

[0490] 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).

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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."

[0497] The present invention relates to a system that generates and provides personalized practice songs tailored to a user's singing skills. This system includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0498] System Overview

[0499] This system first records the user's voice data and sends the recording to a server. The server analyzes the received voice data to evaluate the user's singing skill and generates practice songs based on the evaluation results. The generated practice songs are then provided to the user, allowing them to practice singing efficiently.

[0500] Collecting user voice data

[0501] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[0502] Sending audio data

[0503] The device temporarily stores the recorded audio data and then transmits it to a server via the Internet.

[0504] Analysis of audio data

[0505] The server inputs the received audio data into a voice analysis engine, which analyzes the user's pitch, rhythm, range, tempo, and other parameters in detail to quantify their singing skills, making it clear which areas they are good at and which areas need improvement.

[0506] Etude generation

[0507] Based on the analysis results, the server generates practice songs suited to the user, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty level, and automatically creates songs that encourage the user's growth. For example, songs with minimal fluctuation in scale to stabilize pitch, or songs with many high-pitched parts to strengthen the high range, may be generated.

[0508] Providing practice pieces

[0509] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[0510] Specific examples

[0511] Example 1: Novice user

[0512] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user sings.

[0513] 2. The device will start recording and stop recording when you finish singing.

[0514] 3. The device sends the recorded data to the server.

[0515] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good.

[0516] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0517] 6. The server sends the generated practice piece to the device.

[0518] 7. The user practices using the provided practice songs.

[0519] Example 2: Intermediate user

[0520] 1. The device prompts the user to sing "Let it Go," and the user sings.

[0521] 2. The device will start recording and stop recording when you finish singing.

[0522] 3. The device sends the recorded data to the server.

[0523] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable.

[0524] 5. The server generates an etude with repeated high notes.

[0525] 6. The server sends the generated practice piece to the device.

[0526] 7. The user practices using the provided practice songs.

[0527] This allows users to effectively practice according to their singing ability, and can expect to improve their skills in a short period of time.

[0528] The processing flow will be explained below.

[0529] Step 1:

[0530] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[0531] Step 2:

[0532] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[0533] Step 3:

[0534] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[0535] Step 4:

[0536] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[0537] Step 5:

[0538] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[0539] Step 6:

[0540] The server inputs the received audio data into a voice analysis engine, which performs detailed analysis of pitch, rhythm, range, and tempo.

[0541] Step 7:

[0542] The server evaluates the user's singing skills based on the analysis results, and the evaluation is quantified and saved in the user's skill profile.

[0543] Step 8:

[0544] The server sets parameters for generating practice songs based on the user's singing skill evaluation, including tempo, note range, and difficulty level.

[0545] Step 9:

[0546] The server creates personalized etudes using an etude generation algorithm that constructs the etude according to set parameters.

[0547] Step 10:

[0548] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[0549] Step 11:

[0550] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[0551] Step 12:

[0552] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[0553] Step 13:

[0554] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[0555] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills.

[0556] Example 1

[0557] 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."

[0558] Conventional singing practice systems have difficulty in providing detailed evaluations of individual users' singing skills and providing appropriate practice songs based on those evaluations, limiting the effectiveness of practice. Furthermore, despite technological advances in audio data analysis and practice song generation, there has been no system that effectively combines these technologies. As a result, users have been unable to efficiently and effectively improve their singing skills.

[0559] 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.

[0560] In this invention, the server includes means for recording the user's voice data, means for transmitting the recorded voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice songs suitable for the user based on the evaluation results, means for providing the generated practice songs to the user, means for analyzing the user's voice data based on elements such as pitch, rhythm, range, and tempo and quantifying the evaluation results, and means for using a generative AI model based on the analysis results to create practice songs that are optimal for the user based on prompt sentences, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. This allows users to practice individually and optimally based on their own singing skill, enabling them to effectively improve their skills in a short period of time.

[0561] A "user" is an individual who uses a recording means to provide audio data, receives practice songs, and practices singing.

[0562] A "terminal" is a device used by a user that records audio, transmits audio data to a server, and plays back practice pieces.

[0563] A "server" is a computing device or system that receives and analyzes audio data, and generates and provides optimal practice pieces to users.

[0564] The "recording means" is a function or device for capturing the user's singing voice and saving it as audio data.

[0565] "Transmission means" refers to a function or device for transmitting recorded audio data to a server via the Internet or other communication means.

[0566] The "analysis means" is a function or system that analyzes the received audio data, evaluates elements such as pitch, rhythm, range, and tempo, and quantifies the user's singing skill.

[0567] The "practice piece generation means" is a function or system that generates a practice piece suitable for the user based on the analysis results.

[0568] The "providing means" is a function or device that transmits the created practice piece to the user's terminal and enables the user to play the practice piece.

[0569] "Pitch" is an element that indicates the pitch of each note while the user is singing.

[0570] "Rhythm" is an element that identifies time elements such as the length of notes and beats while the user is singing.

[0571] "Range" is an element that indicates the range of sounds that a user can produce, from the lowest to the highest note.

[0572] "Tempo" is an element that indicates the speed and slowness of the sound being sung.

[0573] The "analysis results" are data that express the evaluation of pitch, rhythm, range, tempo, etc. obtained by the audio data analysis means in numerical or other formats.

[0574] A "generative AI model" is an artificial intelligence algorithm used to generate optimal practice songs that take into account the user's singing skills.

[0575] A "prompt" is an instruction given to a generative AI model, a document that specifies the characteristics and elements of the practice piece to be generated.

[0576] This clearly defines the function and role of each element included in the claims.

[0577] This invention relates to a system that generates and provides practice songs personalized to a user's singing skill. The system mainly includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means. These means work together to provide the user with the practice songs that are most suitable for them. An embodiment of the system is described in detail below.

[0578] Collecting user voice data

[0579] The device has a function for recording the user's singing voice. Specifically, the device is equipped with a microphone function, which the application uses to capture voice data. When the user sings the specified song according to the application's instructions, the voice is recorded.

[0580] example:

[0581] The device will display the instruction "Sing 'Twinkle Twinkle Little Star'" and begin recording.

[0582] The user starts singing and when they finish singing, the recording stops.

[0583] Sending audio data

[0584] The device temporarily stores the recorded audio data and then transmits it to a server. The transmission is via the Internet and the audio data is sent in compressed form. During transmission, checks are performed to ensure the data is intact.

[0585] example:

[0586] The device will display "Recording complete, sending data."

[0587] The terminal compresses the audio data and sends it to the server.

[0588] The server receives the data and returns a confirmation message.

[0589] Analysis of audio data

[0590] The server inputs the received voice data into a voice analysis engine. This engine utilizes a generative AI model to perform detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies the user's singing skill. The results of these analyses are then stored in a database.

[0591] example:

[0592] The server generates a log stating "Analyzing audio data."

[0593] The server divides the audio data into pitch and rhythm and analyzes it.

[0594] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[0595] Etude generation

[0596] Based on the analysis results, the server generates the most suitable practice songs for the user. In this case, a generative AI model is used to automatically create practice songs to improve the user's singing skills, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. The AI ​​model is given appropriate instructions (prompts).

[0597] example:

[0598] The server generates a log saying "Generating appropriate practice pieces."

[0599] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[0600] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[0601] Providing practice pieces

[0602] The generated practice songs are sent from the server to the user's device, which provides the user with an interface that can play the practice songs, allowing the user to listen to the practice songs and practice singing.

[0603] example:

[0604] The server generates a log stating "Sending practice songs to the device."

[0605] The device will display "New practice song received. Would you like to play it?"

[0606] The user selects play and the device plays the practice piece.

[0607] Example prompt

[0608] Below is an example of a prompt sentence that is input to the generative AI model.

[0609] For novice users:

[0610] "The user sang 'Twinkle Twinkle Little Star' and the audio data was sent to the server. Audio analysis revealed that the pitch was unstable but the rhythm was good. Please generate the best practice song for this user."

[0611] For intermediate users:

[0612] "The user sang 'Let it Go' and the audio data was sent to the server. Audio analysis showed that the pitch, rhythm, and tempo were mostly accurate, but the high notes were a little unstable. Please generate an optimal practice song for this user."

[0613] This allows users to effectively practice according to their singing skills, improving their singing skills in a short period of time.

[0614] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] The user follows the application's instructions to sing the specified song, and the device records the audio.

[0617] Specific behavior:

[0618] The device displays the instruction, "Sing 'Twinkle Twinkle Little Star'."

[0619] The user starts singing, and the device uses a microphone to capture the sound and generate audio data.

[0620] Input: User's singing voice

[0621] Output: Recorded audio data

[0622] Step 2:

[0623] The device temporarily stores the recorded audio data and then transmits it over the internet to a server, where checks are performed to ensure data integrity during transmission.

[0624] Specific behavior:

[0625] The device will display "Recording complete, sending data."

[0626] The terminal compresses the audio data and sends it to the server.

[0627] The server receives the data and returns a confirmation message.

[0628] Input: Recorded audio data

[0629] Output: Confirmation message, notification of completion of sending

[0630] Step 3:

[0631] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to perform detailed analysis of pitch, rhythm, range, tempo, and other elements to quantify the user's singing skill.

[0632] Specific behavior:

[0633] The server generates a log stating "Analyzing audio data."

[0634] The server analyzes the audio data, dividing it into pitch and rhythm for analysis.

[0635] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[0636] Input: Received audio data

[0637] Output: Voice analysis results, quantified skill level

[0638] Step 4:

[0639] The server generates practice songs suitable for the user based on the results of audio analysis, using a generative AI model to create the optimal practice songs, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty.

[0640] Specific behavior:

[0641] The server generates a log saying "Generating appropriate practice pieces."

[0642] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[0643] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[0644] Input: Voice analysis results, prompt text

[0645] Output: Generated exercises

[0646] Step 5:

[0647] The server sends the created practice piece to the terminal, which provides the user with an interface that can play the practice piece, allowing the user to listen to the practice piece and practice singing.

[0648] Specific behavior:

[0649] The server generates a log stating "Sending practice songs to the device."

[0650] The device will display "New practice song received. Would you like to play it?"

[0651] The user selects play and the device plays the practice piece.

[0652] Input: Generated Etude

[0653] Output: Practice song to play, user starts singing practice

[0654] By clarifying the specific operations performed at each step and the input / output flow in this way, it becomes easier to understand the processing flow of the entire system and the role of each step.

[0655] (Application example 1)

[0656] 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."

[0657] In recent years, the importance of singing practice tailored to individual needs has increased in the field of music education. However, conventional singing practice systems often lack the functionality to automatically generate and provide appropriate practice songs based on the user's singing skill. They also lack a mechanism to visualize the user's progress and provide specific feedback. This has made it difficult to provide efficient and effective practice.

[0658] 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.

[0659] In this invention, the server includes means for recording the user's voice data, means for transmitting the voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice pieces suitable for the user based on the evaluation results, means for providing the generated practice pieces to the user, means for managing the progress of the user's singing skill based on the analysis results and visualizing the progress, and means for providing specific advice based on the practice results. This not only makes it possible to provide practice pieces that meet the individual needs of the user, but also makes it possible to visualize the user's progress and provide objective and specific feedback.

[0660] "User" refers to an individual who uses this system to practice singing.

[0661] "Audio data" refers to data containing information about audio signals recorded when a user sings.

[0662] "Server" refers to a data center or computing infrastructure for receiving and analyzing audio data, and generating and providing practice pieces.

[0663] "Recording means" means any device or software used to digitally record a user's singing voice.

[0664] "Transmitting means" refers to a network communication means for transferring recorded audio data to a server.

[0665] "Means of analysis" refers to algorithms and software that use an audio analysis engine to perform detailed analysis of pitch, rhythm, range, tempo, etc.

[0666] "Evaluation" refers to quantifying a user's singing skills based on the analysis results.

[0667] "Means for generating" refers to a program or algorithm that automatically creates practice pieces suitable for the user based on the analysis results.

[0668] "Means of providing" refers to the mechanism for sending the generated practice piece to the user's device and making it playable.

[0669] "Progress management tools" refers to systems and features that record and visualize the user's progress in singing skills.

[0670] "Specific advice" refers to feedback such as improvements and recommendations provided to users based on their practice results.

[0671] The present invention relates to a system for generating and providing practice songs personalized to a user's singing skills, including a recording means, a transmitting means, a analyzing means, a evaluating means, a generating means, a providing means, a progress management means, and a means for providing specific advice.

[0672] System Overview

[0673] The entire system starts by recording the user's voice data and sending it to the server. The server receives the voice data and analyzes it using a voice analysis engine. Based on the analysis results, the server evaluates the user's singing skills and generates personalized practice songs according to the evaluation results. The practice songs are then provided to the user's device, which further visualizes the user's progress and provides specific advice.

[0674] Recording audio data

[0675] Users record their singing using a device (smartphone, smart glasses, head-mounted display, etc.), and the recording data is digitally recorded and temporarily stored on the device.

[0676] Sending audio data

[0677] The recorded audio data is transmitted to a server via the Internet using an appropriate network communication means.

[0678] Analysis of audio data

[0679] The server analyzes the received audio data and uses an audio analysis engine (such as librosa or a similar audio analysis library) to perform detailed analysis of the user's pitch, rhythm, range, tempo, and other characteristics, converting them into numerical values.

[0680] Singing skill evaluation

[0681] Based on the analysis, the server evaluates the user's singing skills based on a number of parameters, including pitch stability, rhythmic accuracy, range, and tempo consistency.

[0682] Etude generation

[0683] Based on the evaluation results, the server generates an optimal practice piece for the user, with parameters such as tempo, note range, and difficulty set according to the user's skill level.

[0684] Providing practice pieces

[0685] The generated practice piece is sent from the server to the user's device, which then provides the received practice piece to the user in a playable format.

[0686] Progress management and advice provision

[0687] The server records the user's practice history, visualizes their progress, and provides specific advice based on their practice results, such as "Practice your high-pitched notes more."

[0688] Specific examples

[0689] For example, if a user sings "Twinkle Twinkle Little Star" on their smartphone, the recording is sent to the server for analysis. If the analysis determines that the user's pitch is unstable, a practice song with a slower tempo and less variation in the scale is generated and provided to the user. The user can then practice with the provided practice song to improve their skills.

[0690] Prompt Sentence Examples

[0691] example:

[0692] Analyze the user's audio data (example.wav) and evaluate its tempo, pitch, rhythm, etc. Then, based on the results of this evaluation, generate an optimal practice piece for the user. The generated practice piece should be designed as a simple piece for beginners, with a slow tempo and minimal variation in the scale.

[0693] This example allows the system to provide specific and effective personalized practice songs to help users improve their singing skills.

[0694] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0695] Step 1:

[0696] The user uses the device to record their singing voice. Specifically, they launch the recording application and start singing a designated song, and the audio data is recorded in digital format through the device's built-in microphone. The input is the user's singing voice, and the output is digital audio data.

[0697] Step 2:

[0698] The device temporarily stores the recorded audio data and transmits it to a server via the Internet using a data transfer protocol (e.g. HTTP / HTTPS). The input is digital audio data, and the output is data transmission to the server.

[0699] Step 3:

[0700] The server analyzes the received audio data. Using an audio analysis engine (e.g., librosa library), it performs detailed analysis of characteristics such as pitch, rhythm, range, and tempo. The input is digital audio data, and the output is numerical data containing the analysis results.

[0701] Step 4:

[0702] The server evaluates the user's singing skill based on the analysis results, using parameters such as pitch stability, rhythm accuracy, range, and tempo consistency. The input is numerical data containing the analysis results, and the output is evaluation data that evaluates the user's singing skill.

[0703] Step 5:

[0704] The server generates the most suitable practice pieces for the user based on the evaluation results. It uses a generative AI model to automatically create practice pieces that fit the evaluation data. The input is the evaluation data, and the output is personalized practice piece data.

[0705] Step 6:

[0706] The server then sends the generated exercise to the user's device, converting the exercise data into an appropriate format (e.g., MP3 file) and transmitting it over the Internet. The input is the personalized exercise data, and the output is the transmission of the exercise to the user's device.

[0707] Step 7:

[0708] The user's device plays the received practice songs, and the user practices singing using them. Specifically, the media player function for playing practice songs is used. The input is the practice song data, and the output is audio playback.

[0709] Step 8:

[0710] The server records the user's practice history and stores and analyzes the data to visualize their progress. This allows them to compare their progress with past practice data. The input is the user's practice history data, and the output is the visualized progress data.

[0711] Step 9:

[0712] The server provides specific advice to the user based on the practice results. For example, it generates feedback such as "Practice more on the high-pitched parts" based on the analysis results and sends it to the user. The input is the practice result data, and the output is a specific advice message.

[0713] 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.

[0714] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0715] System Overview

[0716] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[0717] Collecting user voice data

[0718] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[0719] Sending audio data

[0720] The device temporarily stores the recorded audio data in its storage, and then transmits the data to a server via the Internet.

[0721] Analysis of audio data

[0722] The server inputs the received voice data into a voice analysis engine. This engine performs detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies their singing skills. This makes it clear which areas they are good at and which areas need improvement. The emotion engine also recognizes the user's emotions from the voice data and quantifies them.

[0723] Etude generation

[0724] The server generates practice songs suited to the user based on the results of voice analysis and emotion recognition. It automatically creates songs that encourage the user's growth by taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotions. For example, if the user seems sad, it will generate a song with a slow tempo, and if they seem excited, it will generate a song with a fast tempo.

[0725] Providing practice pieces

[0726] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[0727] Specific examples

[0728] Example 1: Novice user

[0729] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[0730] 2. The device will start recording and stop recording when you finish singing.

[0731] 3. The device sends the recorded data to the server.

[0732] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion engine also determines that the user is relaxed.

[0733] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0734] 6. The server sends the generated practice piece to the device.

[0735] 7. The user practices using the provided practice songs.

[0736] Example 2: Intermediate user

[0737] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[0738] 2. The device will start recording and stop recording when you finish singing.

[0739] 3. The device sends the recorded data to the server.

[0740] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. The emotion engine also determines that the user is excited.

[0741] 5. The server generates a fast-paced etude with repeated high notes.

[0742] 6. The server sends the generated practice piece to the device.

[0743] 7. The user practices using the provided practice songs.

[0744] This allows users to effectively practice according to their singing skills and emotional state.

[0745] The processing flow will be explained below.

[0746] Step 1:

[0747] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[0748] Step 2:

[0749] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[0750] Step 3:

[0751] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[0752] Step 4:

[0753] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[0754] Step 5:

[0755] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[0756] Step 6:

[0757] The server inputs the received audio data into a voice analysis engine, which analyzes the pitch, rhythm, range, and tempo in detail and generates numerical evaluation data.

[0758] Step 7:

[0759] The server also inputs the voice data into the emotion engine, which recognizes the user's emotion based on features extracted from the voice and generates emotion evaluation data.

[0760] Step 8:

[0761] The server combines data from both the voice analysis engine and the emotion engine to evaluate the user's singing skill and emotional state, and stores the results of these evaluations as a numerical value in the user profile.

[0762] Step 9:

[0763] Based on the evaluation results, the server sets parameters to generate practice pieces suited to the user, including tempo, range of notes, difficulty level, and emotional state.

[0764] Step 10:

[0765] The server uses a practice song generation algorithm to create practice songs according to the set parameters, such as songs for stabilizing pitch or songs with tempo adjustments based on the user's emotional state.

[0766] Step 11:

[0767] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[0768] Step 12:

[0769] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[0770] Step 13:

[0771] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[0772] Step 14:

[0773] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[0774] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills and emotions.

[0775] Example 2

[0776] 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."

[0777] Conventional singing practice systems have been unable to provide practice songs that take into account not only the user's singing skill but also their emotional and psychological state. As a result, users are unable to practice effectively to improve their singing skills, and it is difficult to provide a practice experience that suits each individual's emotional state. There is also a need for a system that can clearly identify the user's specific weaknesses and challenges and generate practice songs that are suited to them.

[0778] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0779] In this invention, the server includes means for analyzing the voice data and evaluating the user's singing skill and emotions, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece to the user, thereby enabling the provision of practice pieces that take into account both the user's singing skill and emotional state.

[0780] "User's voice data" refers to voice information sung by a user, and is digital data collected by a recording device.

[0781] "Recording means" refers to devices or software that record audio as digital data.

[0782] "Electronic computer" refers to a computer or server that has the ability to receive, process, store, and analyze voice data.

[0783] "Means for analyzing" refers to software or algorithms for analyzing digitized audio data and extracting specific parameters.

[0784] "Singing skill" is a numerical representation of a user's musical ability, such as pitch, rhythm, range, and tempo.

[0785] "Emotion" refers to the psychological state and mood that the user has when singing, and is information analyzed from audio data.

[0786] "Means of evaluation" refers to the process of quantifying a user's singing skills and emotions based on analyzed audio data.

[0787] "Means for generating practice songs" refers to algorithms or software that automatically generate new practice songs based on the user's singing skill and emotions.

[0788] "Means provided to the user" refers to an interface or application that allows the user to play the generated practice piece.

[0789] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0790] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[0791] Collecting user voice data

[0792] The device uses a built-in or externally connected microphone to record the user's singing voice. When the user sings the designated song following the system's instructions, the voice is recorded and saved as high-quality digital data.

[0793] Sending audio data

[0794] The device reads the recorded audio data from the storage and transmits it to the server over the Internet, using secure protocols such as SSL / TLS to protect the confidentiality and integrity of the data.

[0795] Analysis of audio data

[0796] The server inputs the received voice data into an analysis engine (e.g., a general voice analysis engine). This engine performs detailed analysis of the user's skill elements, such as pitch, rhythm, range, and tempo, and generates a skill score. For emotion analysis, a separate emotion engine (e.g., a general emotion analysis engine) is used to recognize the user's emotions and quantify the results.

[0797] Etude generation

[0798] The server then uses these analysis results to run a music generation algorithm (for example, an algorithm built using a Python music generation library) to generate practice pieces. The algorithm creates pieces based on parameters such as pitch, rhythm, range, tempo, and difficulty. It also takes into account the user's emotional state; for example, if the user is sad, it generates a slower song, and if the user is excited, it generates a faster song.

[0799] Providing practice pieces

[0800] The generated practice songs are sent from the server to the terminal, and the terminal provides the user with an interface for playing the received practice songs, allowing the user to practice singing using the provided practice songs.

[0801] Specific examples

[0802] Example 1: Novice user

[0803] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[0804] 2. The device will start recording and stop recording when you finish singing.

[0805] 3. The device sends the recorded data to the server.

[0806] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. Emotion analysis also determines that the user is relaxed.

[0807] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0808] 6. The server sends the generated practice piece to the device.

[0809] 7. The user practices using the provided practice songs.

[0810] Example 2: Intermediate user

[0811] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[0812] 2. The device will start recording and stop recording when you finish singing.

[0813] 3. The device sends the recorded data to the server.

[0814] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. Emotion analysis also determines that the user is excited.

[0815] 5. The server generates a fast-paced etude with repeated high notes.

[0816] 6. The server sends the generated practice piece to the device.

[0817] 7. The user practices using the provided practice songs.

[0818] This system allows users to effectively practice according to their singing skills and emotional state.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] The user logs into the system and selects a song to sing.

[0822] Input: Username and password, list of practice songs

[0823] Data processing: User authentication (database matching), acquisition of practice song list

[0824] Output: Authentication success message, practice song list

[0825] Specific operation: The user logs in by entering their username and password on the device. If login is successful, the device retrieves a list of practice songs from the server and displays it to the user. The user then selects the song they want to practice from the displayed list.

[0826] Step 2:

[0827] The device records the user's voice.

[0828] Input: User's singing voice

[0829] Data processing: Digital conversion of audio, high-quality recording, noise canceling processing

[0830] Output: Recorded audio data

[0831] How it works: When a user starts singing along with a selected song, the device will record the sound using a built-in or externally connected microphone. This recording will then be temporarily stored as digital data in storage.

[0832] Step 3:

[0833] The device sends the recorded data to the server.

[0834] Input: Pre-recorded audio data

[0835] Data processing: Reading audio data and preparing it for transmission, encryption via SSL / TLS

[0836] Output: Notification of completion of transmission to the server

[0837] Specific operation: The recorded audio data is read from the storage and sent to the server via the Internet. When sending the data, confidentiality is maintained using secure protocols such as SSL / TLS.

[0838] Step 4:

[0839] The server analyzes the audio data and evaluates singing skill and emotion.

[0840] Input: Audio data

[0841] Data processing: Analysis of pitch, rhythm, range, tempo, emotion recognition and quantification

[0842] Output: Singing skill score and emotion score

[0843] Specific operation: After receiving the voice data, the server inputs the data into a voice analysis engine (e.g., a general voice analysis engine) to perform a detailed analysis of the user's singing skills. After that, it uses an emotion analysis engine to recognize the user's emotional state and quantify each of them.

[0844] Step 5:

[0845] The server generates practice pieces based on the analysis results.

[0846] Input: Singing skill score and emotion score

[0847] Data processing: Execution of music generation algorithms, automatic generation of practice songs

[0848] Output: Generated practice piece data

[0849] How it works: Based on the analysis results, the server runs a music generation algorithm using Python to automatically generate practice pieces optimized for the user's skill and emotions. Parameters such as tempo, range, and difficulty are adjusted to generate songs that also take the user's emotional state into account.

[0850] Step 6:

[0851] The server sends the generated practice piece to the terminal.

[0852] Input: Generated etude data

[0853] Data processing: Reading and sending practice song data, encryption by SSL / TLS

[0854] Output: Notification of completion of transmission to the terminal

[0855] Specific operation: The generated practice piece is temporarily stored and then sent to the device via the Internet. When sending data, confidentiality is maintained using secure protocols such as SSL / TLS.

[0856] Step 7:

[0857] The device plays the practice songs and provides them to the user.

[0858] Input: Received practice piece data

[0859] Data processing: Preparation for playback, display of playback interface

[0860] Output: Practice piece played, user feedback

[0861] Specific operation: The device provides an interface for playing the received practice songs, and the user can practice singing while playing the songs on the device. The interface also has loop playback and speed adjustment functions to support the user's practice experience.

[0862] (Application example 2)

[0863] 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."

[0864] Existing singing practice systems focus only on the user's singing skills and do not take into account their emotional state. This makes it difficult for users to practice in a way that suits their emotions. Furthermore, the practice songs provided to users are not individually customized, making it difficult for users to maintain their growth and motivation. Furthermore, the lack of effective feedback makes it difficult for users to understand where they need to improve.

[0865] The specific processing by the specific 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 means for analyzing the user's voice data and evaluating the singing skill and emotion, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece using a prompt sentence. This allows the user to use a practice piece individually customized according to their singing skill and emotional state, enabling effective practice that helps them maintain motivation.

[0866] A "user" is a person who uses this system to practice singing.

[0867] "Audio data" refers to audio information recorded when a user sings.

[0868] A "server" is a computer system that receives audio data, analyzes it, and generates practice pieces.

[0869] "Evaluation" refers to the use of analytical means to quantify and judge the user's singing skill and emotional state.

[0870] "Etudes" are pieces of music generated according to the user's singing skills and emotions.

[0871] The "analysis means" is a system that has the function of analyzing and quantifying the user's singing skills and emotions from audio data.

[0872] "Emotion" refers to the psychological state of the user while singing, and is quantified by analytical means.

[0873] "Providing" means sending the generated practice piece to the user's device so that the user can practice it.

[0874] A "prompt" is a sentence that instructs the user on the next action or exercise to be taken.

[0875] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. Detailed embodiments of this system will be described below.

[0876] System Overview

[0877] This system records the user's voice data and sends the recorded data to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing effectively.

[0878] Hardware and Software

[0879] Terminal: A device that records and plays audio data and communicates with a server. Examples include smartphones, tablets, and head-mounted displays.

[0880] Server: A computer system that analyzes and evaluates audio data and generates practice pieces. The main software used is a voice analysis engine (e.g., librosa), an emotion recognition engine (e.g., EmotionRecognition), and a practice piece generation engine (e.g., generative AI model).

[0881] Communication method: An internet connection to transfer data between your device and our server.

[0882] Recording and transmitting audio data

[0883] The user uses the device to sing a designated song according to a specific guide, and the device records this voice and transmits the recorded data to a server via the Internet.

[0884] Analysis and evaluation of speech data

[0885] The server inputs the received voice data into a voice analysis engine, which analyzes and quantifies the user's pitch, rhythm, range, and tempo in detail. It also uses an emotion recognition engine to analyze the user's emotional state from the voice data and quantifies this as well.

[0886] Etude generation

[0887] Based on the results of voice analysis and emotion recognition, the server generates practice songs suited to the user. The song is automatically created taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotional state. This allows the server to provide a fast-tempo song if the user is excited, and a slower-tempo song if the user is relaxed.

[0888] Providing practice pieces

[0889] The generated practice pieces are sent from the server to the user's device, which provides an interface for playing the received practice pieces to support the practice. For playback, a library such as Pydub is used.

[0890] Specific use cases

[0891] For novice users

[0892] 1. The device prompts the user to sing a "simple song."

[0893] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[0894] 3. The device sends the recorded data to the server.

[0895] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion recognition engine also determines that the user is relaxed.

[0896] 5. The server generates the appropriate practice piece and sends it to the user's device.

[0897] 6. The user practices using the practice songs provided.

[0898] For intermediate users

[0899] 1. The device prompts the user to sing a "medium difficulty song."

[0900] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[0901] 3. The device sends the recorded data to the server.

[0902] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are unstable. The emotion recognition engine also determines that the user is excited.

[0903] 5. The server generates a fast-paced practice piece with repeated high notes and sends it to the user's device.

[0904] 6. The user practices using the practice songs provided.

[0905] Prompt Sentence Examples

[0906] Design a system that analyzes a user's singing audio data and generates personalized practice songs. Use the following information:

[0907] Records audio data and sends it to the server

[0908] Server-side voice analysis evaluates the user's singing skill and emotional state

[0909] Generates customized practice pieces based on user analysis results

[0910] Provides practice pieces and displays a guiding interface for users

[0911] Examples:

[0912] 1. Beginner users should sing "easy songs."

[0913] 2. Intermediate users sing "songs of medium difficulty."

[0914] In this way, the system can provide effective singing practice according to the user's singing skill and emotions.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The user begins singing.

[0918] The device displays a guide message to the user and instructs them to sing a specified song. The input is the user's real-time voice, and the output is a trigger to start recording.

[0919] Step 2:

[0920] The device records the user's singing voice.

[0921] When the user starts singing, the device records the voice data using the built-in microphone. The input is the user's singing voice (analog signal), and the output is the recorded digital voice file (e.g., WAV format).

[0922] Step 3:

[0923] Once the recording is complete, the device sends the audio data to the server.

[0924] When the user finishes singing, the device temporarily stores the audio data and sends it to a server via the Internet. The input is a digital audio file, and the output is a status indicating that data transfer to the server has been completed.

[0925] Step 4:

[0926] The server analyzes the received audio data.

[0927] The server inputs the received audio data into an analysis engine (e.g., librosa), which analyzes and quantifies the pitch, rhythm, range, and tempo in detail. The input is a digital audio file, and the output is quantified singing skill data.

[0928] Step 5:

[0929] The server evaluates the emotion using an emotion recognition engine.

[0930] The server inputs the analyzed voice data into an emotion recognition engine (e.g., EmotionRecognition), which analyzes and quantifies the user's emotional state. The input is quantified singing skill data, and the output is quantified emotional data.

[0931] Step 6:

[0932] The server generates practice songs based on singing skill and emotional state.

[0933] The server provides singing skill data and emotional data as input to the generative AI model, which then generates the optimal practice song for the user. The input is singing skill data and emotional data, and the output is a digital file of the generated practice song.

[0934] Step 7:

[0935] The server transmits the generated practice piece to the terminal.

[0936] The server sends the generated practice piece to the user's device. The input is the digital file of the created practice piece, and the output is the status of the completion of data transfer to the device.

[0937] Step 8:

[0938] The device provides the practice songs to the user and plays them.

[0939] The device displays an interface for playing the received practice piece and prompts the user to practice. The input is the generated digital file of the practice piece, and the output is the user's practice behavior.

[0940] Through the above steps, the user can practice singing effectively using practice songs that are individually customized according to the user's singing skill and emotional state.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] [Third embodiment]

[0945] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0946] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0947] 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).

[0948] 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.

[0949] 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.

[0950] 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).

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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."

[0957] The present invention relates to a system that generates and provides personalized practice songs tailored to a user's singing skills. This system includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[0958] System Overview

[0959] This system first records the user's voice data and sends the recording to a server. The server analyzes the received voice data to evaluate the user's singing skill and generates practice songs based on the evaluation results. The generated practice songs are then provided to the user, allowing them to practice singing efficiently.

[0960] Collecting user voice data

[0961] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[0962] Sending audio data

[0963] The device temporarily stores the recorded audio data and then transmits it to a server via the Internet.

[0964] Analysis of audio data

[0965] The server inputs the received audio data into a voice analysis engine, which analyzes the user's pitch, rhythm, range, tempo, and other parameters in detail to quantify their singing skills, making it clear which areas they are good at and which areas need improvement.

[0966] Etude generation

[0967] Based on the analysis results, the server generates practice songs suited to the user, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty level, and automatically creates songs that encourage the user's growth. For example, songs with minimal fluctuation in scale to stabilize pitch, or songs with many high-pitched parts to strengthen the high range, may be generated.

[0968] Providing practice pieces

[0969] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[0970] Specific examples

[0971] Example 1: Novice user

[0972] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user sings.

[0973] 2. The device will start recording and stop recording when you finish singing.

[0974] 3. The device sends the recorded data to the server.

[0975] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good.

[0976] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[0977] 6. The server sends the generated practice piece to the device.

[0978] 7. The user practices using the provided practice songs.

[0979] Example 2: Intermediate user

[0980] 1. The device prompts the user to sing "Let it Go," and the user sings.

[0981] 2. The device will start recording and stop recording when you finish singing.

[0982] 3. The device sends the recorded data to the server.

[0983] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable.

[0984] 5. The server generates an etude with repeated high notes.

[0985] 6. The server sends the generated practice piece to the device.

[0986] 7. The user practices using the provided practice songs.

[0987] This allows users to effectively practice according to their singing ability, and can expect to improve their skills in a short period of time.

[0988] The processing flow will be explained below.

[0989] Step 1:

[0990] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[0991] Step 2:

[0992] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[0993] Step 3:

[0994] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[0995] Step 4:

[0996] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[0997] Step 5:

[0998] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[0999] Step 6:

[1000] The server inputs the received audio data into a voice analysis engine, which performs detailed analysis of pitch, rhythm, range, and tempo.

[1001] Step 7:

[1002] The server evaluates the user's singing skills based on the analysis results, and the evaluation is quantified and saved in the user's skill profile.

[1003] Step 8:

[1004] The server sets parameters for generating practice songs based on the user's singing skill evaluation, including tempo, note range, and difficulty level.

[1005] Step 9:

[1006] The server creates personalized etudes using an etude generation algorithm that constructs the etude according to set parameters.

[1007] Step 10:

[1008] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[1009] Step 11:

[1010] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[1011] Step 12:

[1012] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[1013] Step 13:

[1014] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[1015] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills.

[1016] Example 1

[1017] 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."

[1018] Conventional singing practice systems have difficulty in providing detailed evaluations of individual users' singing skills and providing appropriate practice songs based on those evaluations, limiting the effectiveness of practice. Furthermore, despite technological advances in audio data analysis and practice song generation, there has been no system that effectively combines these technologies. As a result, users have been unable to efficiently and effectively improve their singing skills.

[1019] 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.

[1020] In this invention, the server includes means for recording the user's voice data, means for transmitting the recorded voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice songs suitable for the user based on the evaluation results, means for providing the generated practice songs to the user, means for analyzing the user's voice data based on elements such as pitch, rhythm, range, and tempo and quantifying the evaluation results, and means for using a generative AI model based on the analysis results to create practice songs that are optimal for the user based on prompt sentences, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. This allows users to practice individually and optimally based on their own singing skill, enabling them to effectively improve their skills in a short period of time.

[1021] A "user" is an individual who uses a recording means to provide audio data, receives practice songs, and practices singing.

[1022] A "terminal" is a device used by a user that records audio, transmits audio data to a server, and plays back practice pieces.

[1023] A "server" is a computing device or system that receives and analyzes audio data, and generates and provides optimal practice pieces to users.

[1024] The "recording means" is a function or device for capturing the user's singing voice and saving it as audio data.

[1025] "Transmission means" refers to a function or device for transmitting recorded audio data to a server via the Internet or other communication means.

[1026] The "analysis means" is a function or system that analyzes the received audio data, evaluates elements such as pitch, rhythm, range, and tempo, and quantifies the user's singing skill.

[1027] The "practice piece generation means" is a function or system that generates a practice piece suitable for the user based on the analysis results.

[1028] The "providing means" is a function or device that transmits the created practice piece to the user's terminal and enables the user to play the practice piece.

[1029] "Pitch" is an element that indicates the pitch of each note while the user is singing.

[1030] "Rhythm" is an element that identifies time elements such as the length of notes and beats while the user is singing.

[1031] "Range" is an element that indicates the range of sounds that a user can produce, from the lowest to the highest note.

[1032] "Tempo" is an element that indicates the speed and slowness of the sound being sung.

[1033] The "analysis results" are data that express the evaluation of pitch, rhythm, range, tempo, etc. obtained by the audio data analysis means in numerical or other formats.

[1034] A "generative AI model" is an artificial intelligence algorithm used to generate optimal practice songs that take into account the user's singing skills.

[1035] A "prompt" is an instruction given to a generative AI model, a document that specifies the characteristics and elements of the practice piece to be generated.

[1036] This clearly defines the function and role of each element included in the claims.

[1037] This invention relates to a system that generates and provides practice songs personalized to a user's singing skill. The system mainly includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means. These means work together to provide the user with the practice songs that are most suitable for them. An embodiment of the system is described in detail below.

[1038] Collecting user voice data

[1039] The device has a function for recording the user's singing voice. Specifically, the device is equipped with a microphone function, which the application uses to capture voice data. When the user sings the specified song according to the application's instructions, the voice is recorded.

[1040] example:

[1041] The device will display the instruction "Sing 'Twinkle Twinkle Little Star'" and begin recording.

[1042] The user starts singing and when they finish singing, the recording stops.

[1043] Sending audio data

[1044] The device temporarily stores the recorded audio data and then transmits it to a server. The transmission is via the Internet and the audio data is sent in compressed form. During transmission, checks are performed to ensure the data is intact.

[1045] example:

[1046] The device will display "Recording complete, sending data."

[1047] The terminal compresses the audio data and sends it to the server.

[1048] The server receives the data and returns a confirmation message.

[1049] Analysis of audio data

[1050] The server inputs the received voice data into a voice analysis engine. This engine utilizes a generative AI model to perform detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies the user's singing skill. The results of these analyses are then stored in a database.

[1051] example:

[1052] The server generates a log stating "Analyzing audio data."

[1053] The server divides the audio data into pitch and rhythm and analyzes it.

[1054] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[1055] Etude generation

[1056] Based on the analysis results, the server generates the most suitable practice songs for the user. In this case, a generative AI model is used to automatically create practice songs to improve the user's singing skills, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. The AI ​​model is given appropriate instructions (prompts).

[1057] example:

[1058] The server generates a log saying "Generating appropriate practice pieces."

[1059] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[1060] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[1061] Providing practice pieces

[1062] The generated practice songs are sent from the server to the user's device, which provides the user with an interface that can play the practice songs, allowing the user to listen to the practice songs and practice singing.

[1063] example:

[1064] The server generates a log stating "Sending practice songs to the device."

[1065] The device will display "New practice song received. Would you like to play it?"

[1066] The user selects play and the device plays the practice piece.

[1067] Example prompt

[1068] Below is an example of a prompt sentence that is input to the generative AI model.

[1069] For novice users:

[1070] "The user sang 'Twinkle Twinkle Little Star' and the audio data was sent to the server. Audio analysis revealed that the pitch was unstable but the rhythm was good. Please generate the best practice song for this user."

[1071] For intermediate users:

[1072] "The user sang 'Let it Go' and the audio data was sent to the server. Audio analysis showed that the pitch, rhythm, and tempo were mostly accurate, but the high notes were a little unstable. Please generate an optimal practice song for this user."

[1073] This allows users to effectively practice according to their singing skills, improving their singing skills in a short period of time.

[1074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1075] Step 1:

[1076] The user follows the application's instructions to sing the specified song, and the device records the audio.

[1077] Specific behavior:

[1078] The device displays the instruction, "Sing 'Twinkle Twinkle Little Star'."

[1079] The user starts singing, and the device uses a microphone to capture the sound and generate audio data.

[1080] Input: User's singing voice

[1081] Output: Recorded audio data

[1082] Step 2:

[1083] The device temporarily stores the recorded audio data and then transmits it over the internet to a server, where checks are performed to ensure data integrity during transmission.

[1084] Specific behavior:

[1085] The device will display "Recording complete, sending data."

[1086] The terminal compresses the audio data and sends it to the server.

[1087] The server receives the data and returns a confirmation message.

[1088] Input: Recorded audio data

[1089] Output: Confirmation message, notification of completion of sending

[1090] Step 3:

[1091] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to perform detailed analysis of pitch, rhythm, range, tempo, and other elements to quantify the user's singing skill.

[1092] Specific behavior:

[1093] The server generates a log stating "Analyzing audio data."

[1094] The server analyzes the audio data, dividing it into pitch and rhythm for analysis.

[1095] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[1096] Input: Received audio data

[1097] Output: Voice analysis results, quantified skill level

[1098] Step 4:

[1099] The server generates practice songs suitable for the user based on the results of audio analysis, using a generative AI model to create the optimal practice songs, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty.

[1100] Specific behavior:

[1101] The server generates a log saying "Generating appropriate practice pieces."

[1102] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[1103] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[1104] Input: Voice analysis results, prompt text

[1105] Output: Generated exercises

[1106] Step 5:

[1107] The server sends the created practice piece to the terminal, which provides the user with an interface that can play the practice piece, allowing the user to listen to the practice piece and practice singing.

[1108] Specific behavior:

[1109] The server generates a log stating "Sending practice songs to the device."

[1110] The device will display "New practice song received. Would you like to play it?"

[1111] The user selects play and the device plays the practice piece.

[1112] Input: Generated Etude

[1113] Output: Practice song to play, user starts singing practice

[1114] By clarifying the specific operations performed at each step and the input / output flow in this way, it becomes easier to understand the processing flow of the entire system and the role of each step.

[1115] (Application example 1)

[1116] 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."

[1117] In recent years, the importance of singing practice tailored to individual needs has increased in the field of music education. However, conventional singing practice systems often lack the functionality to automatically generate and provide appropriate practice songs based on the user's singing skill. They also lack a mechanism to visualize the user's progress and provide specific feedback. This has made it difficult to provide efficient and effective practice.

[1118] 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.

[1119] In this invention, the server includes means for recording the user's voice data, means for transmitting the voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice pieces suitable for the user based on the evaluation results, means for providing the generated practice pieces to the user, means for managing the progress of the user's singing skill based on the analysis results and visualizing the progress, and means for providing specific advice based on the practice results. This not only makes it possible to provide practice pieces that meet the individual needs of the user, but also makes it possible to visualize the user's progress and provide objective and specific feedback.

[1120] "User" refers to an individual who uses this system to practice singing.

[1121] "Audio data" refers to data containing information about audio signals recorded when a user sings.

[1122] "Server" refers to a data center or computing infrastructure for receiving and analyzing audio data, and generating and providing practice pieces.

[1123] "Recording means" means any device or software used to digitally record a user's singing voice.

[1124] "Transmitting means" refers to a network communication means for transferring recorded audio data to a server.

[1125] "Means of analysis" refers to algorithms and software that use an audio analysis engine to perform detailed analysis of pitch, rhythm, range, tempo, etc.

[1126] "Evaluation" refers to quantifying a user's singing skills based on the analysis results.

[1127] "Means for generating" refers to a program or algorithm that automatically creates practice pieces suitable for the user based on the analysis results.

[1128] "Means of providing" refers to the mechanism for sending the generated practice piece to the user's device and making it playable.

[1129] "Progress management tools" refers to systems and features that record and visualize the user's progress in singing skills.

[1130] "Specific advice" refers to feedback such as improvements and recommendations provided to users based on their practice results.

[1131] The present invention relates to a system for generating and providing practice songs personalized to a user's singing skills, including a recording means, a transmitting means, a analyzing means, a evaluating means, a generating means, a providing means, a progress management means, and a means for providing specific advice.

[1132] System Overview

[1133] The entire system starts by recording the user's voice data and sending it to the server. The server receives the voice data and analyzes it using a voice analysis engine. Based on the analysis results, the server evaluates the user's singing skills and generates personalized practice songs according to the evaluation results. The practice songs are then provided to the user's device, which further visualizes the user's progress and provides specific advice.

[1134] Recording audio data

[1135] Users record their singing using a device (smartphone, smart glasses, head-mounted display, etc.), and the recording data is digitally recorded and temporarily stored on the device.

[1136] Sending audio data

[1137] The recorded audio data is transmitted to a server via the Internet using an appropriate network communication means.

[1138] Analysis of audio data

[1139] The server analyzes the received audio data and uses an audio analysis engine (such as librosa or a similar audio analysis library) to perform detailed analysis of the user's pitch, rhythm, range, tempo, and other characteristics, converting them into numerical values.

[1140] Singing skill evaluation

[1141] Based on the analysis, the server evaluates the user's singing skills based on a number of parameters, including pitch stability, rhythmic accuracy, range, and tempo consistency.

[1142] Etude generation

[1143] Based on the evaluation results, the server generates an optimal practice piece for the user, with parameters such as tempo, note range, and difficulty set according to the user's skill level.

[1144] Providing practice pieces

[1145] The generated practice piece is sent from the server to the user's device, which then provides the received practice piece to the user in a playable format.

[1146] Progress management and advice provision

[1147] The server records the user's practice history, visualizes their progress, and provides specific advice based on their practice results, such as "Practice your high-pitched notes more."

[1148] Specific examples

[1149] For example, if a user sings "Twinkle Twinkle Little Star" on their smartphone, the recording is sent to the server for analysis. If the analysis determines that the user's pitch is unstable, a practice song with a slower tempo and less variation in the scale is generated and provided to the user. The user can then practice with the provided practice song to improve their skills.

[1150] Prompt Sentence Examples

[1151] example:

[1152] Analyze the user's audio data (example.wav) and evaluate its tempo, pitch, rhythm, etc. Then, based on the results of this evaluation, generate an optimal practice piece for the user. The generated practice piece should be designed as a simple piece for beginners, with a slow tempo and minimal variation in the scale.

[1153] This example allows the system to provide specific and effective personalized practice songs to help users improve their singing skills.

[1154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1155] Step 1:

[1156] The user uses the device to record their singing voice. Specifically, they launch the recording application and start singing a designated song, and the audio data is recorded in digital format through the device's built-in microphone. The input is the user's singing voice, and the output is digital audio data.

[1157] Step 2:

[1158] The device temporarily stores the recorded audio data and transmits it to a server via the Internet using a data transfer protocol (e.g. HTTP / HTTPS). The input is digital audio data, and the output is data transmission to the server.

[1159] Step 3:

[1160] The server analyzes the received audio data. Using an audio analysis engine (e.g., librosa library), it performs detailed analysis of characteristics such as pitch, rhythm, range, and tempo. The input is digital audio data, and the output is numerical data containing the analysis results.

[1161] Step 4:

[1162] The server evaluates the user's singing skill based on the analysis results, using parameters such as pitch stability, rhythm accuracy, range, and tempo consistency. The input is numerical data containing the analysis results, and the output is evaluation data that evaluates the user's singing skill.

[1163] Step 5:

[1164] The server generates the most suitable practice pieces for the user based on the evaluation results. It uses a generative AI model to automatically create practice pieces that fit the evaluation data. The input is the evaluation data, and the output is personalized practice piece data.

[1165] Step 6:

[1166] The server then sends the generated exercise to the user's device, converting the exercise data into an appropriate format (e.g., MP3 file) and transmitting it over the Internet. The input is the personalized exercise data, and the output is the transmission of the exercise to the user's device.

[1167] Step 7:

[1168] The user's device plays the received practice songs, and the user practices singing using them. Specifically, the media player function for playing practice songs is used. The input is the practice song data, and the output is audio playback.

[1169] Step 8:

[1170] The server records the user's practice history and stores and analyzes the data to visualize their progress. This allows them to compare their progress with past practice data. The input is the user's practice history data, and the output is the visualized progress data.

[1171] Step 9:

[1172] The server provides specific advice to the user based on the practice results. For example, it generates feedback such as "Practice more on the high-pitched parts" based on the analysis results and sends it to the user. The input is the practice result data, and the output is a specific advice message.

[1173] 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.

[1174] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[1175] System Overview

[1176] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[1177] Collecting user voice data

[1178] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[1179] Sending audio data

[1180] The device temporarily stores the recorded audio data in its storage, and then transmits the data to a server via the Internet.

[1181] Analysis of audio data

[1182] The server inputs the received voice data into a voice analysis engine. This engine performs detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies their singing skills. This makes it clear which areas they are good at and which areas need improvement. The emotion engine also recognizes the user's emotions from the voice data and quantifies them.

[1183] Etude generation

[1184] The server generates practice songs suited to the user based on the results of voice analysis and emotion recognition. It automatically creates songs that encourage the user's growth by taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotions. For example, if the user seems sad, it will generate a song with a slow tempo, and if they seem excited, it will generate a song with a fast tempo.

[1185] Providing practice pieces

[1186] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[1187] Specific examples

[1188] Example 1: Novice user

[1189] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[1190] 2. The device will start recording and stop recording when you finish singing.

[1191] 3. The device sends the recorded data to the server.

[1192] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion engine also determines that the user is relaxed.

[1193] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[1194] 6. The server sends the generated practice piece to the device.

[1195] 7. The user practices using the provided practice songs.

[1196] Example 2: Intermediate user

[1197] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[1198] 2. The device will start recording and stop recording when you finish singing.

[1199] 3. The device sends the recorded data to the server.

[1200] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. The emotion engine also determines that the user is excited.

[1201] 5. The server generates a fast-paced etude with repeated high notes.

[1202] 6. The server sends the generated practice piece to the device.

[1203] 7. The user practices using the provided practice songs.

[1204] This allows users to effectively practice according to their singing skills and emotional state.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[1208] Step 2:

[1209] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[1210] Step 3:

[1211] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[1212] Step 4:

[1213] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[1214] Step 5:

[1215] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[1216] Step 6:

[1217] The server inputs the received audio data into a voice analysis engine, which analyzes the pitch, rhythm, range, and tempo in detail and generates numerical evaluation data.

[1218] Step 7:

[1219] The server also inputs the voice data into the emotion engine, which recognizes the user's emotion based on features extracted from the voice and generates emotion evaluation data.

[1220] Step 8:

[1221] The server combines data from both the voice analysis engine and the emotion engine to evaluate the user's singing skill and emotional state, and stores the results of these evaluations as a numerical value in the user profile.

[1222] Step 9:

[1223] Based on the evaluation results, the server sets parameters to generate practice pieces suited to the user, including tempo, range of notes, difficulty level, and emotional state.

[1224] Step 10:

[1225] The server uses a practice song generation algorithm to create practice songs according to the set parameters, such as songs for stabilizing pitch or songs with tempo adjustments based on the user's emotional state.

[1226] Step 11:

[1227] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[1228] Step 12:

[1229] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[1230] Step 13:

[1231] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[1232] Step 14:

[1233] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[1234] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills and emotions.

[1235] Example 2

[1236] 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."

[1237] Conventional singing practice systems have been unable to provide practice songs that take into account not only the user's singing skill but also their emotional and psychological state. As a result, users are unable to practice effectively to improve their singing skills, and it is difficult to provide a practice experience that suits each individual's emotional state. There is also a need for a system that can clearly identify the user's specific weaknesses and challenges and generate practice songs that are suited to them.

[1238] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1239] In this invention, the server includes means for analyzing the voice data and evaluating the user's singing skill and emotions, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece to the user, thereby enabling the provision of practice pieces that take into account both the user's singing skill and emotional state.

[1240] "User's voice data" refers to voice information sung by a user, and is digital data collected by a recording device.

[1241] "Recording means" refers to devices or software that record audio as digital data.

[1242] "Electronic computer" refers to a computer or server that has the ability to receive, process, store, and analyze voice data.

[1243] "Means for analyzing" refers to software or algorithms for analyzing digitized audio data and extracting specific parameters.

[1244] "Singing skill" is a numerical representation of a user's musical ability, such as pitch, rhythm, range, and tempo.

[1245] "Emotion" refers to the psychological state and mood that the user has when singing, and is information analyzed from audio data.

[1246] "Means of evaluation" refers to the process of quantifying a user's singing skills and emotions based on analyzed audio data.

[1247] "Means for generating practice songs" refers to algorithms or software that automatically generate new practice songs based on the user's singing skill and emotions.

[1248] "Means provided to the user" refers to an interface or application that allows the user to play the generated practice piece.

[1249] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[1250] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[1251] Collecting user voice data

[1252] The device uses a built-in or externally connected microphone to record the user's singing voice. When the user sings the designated song following the system's instructions, the voice is recorded and saved as high-quality digital data.

[1253] Sending audio data

[1254] The device reads the recorded audio data from the storage and transmits it to the server over the Internet, using secure protocols such as SSL / TLS to protect the confidentiality and integrity of the data.

[1255] Analysis of audio data

[1256] The server inputs the received voice data into an analysis engine (e.g., a general voice analysis engine). This engine performs detailed analysis of the user's skill elements, such as pitch, rhythm, range, and tempo, and generates a skill score. For emotion analysis, a separate emotion engine (e.g., a general emotion analysis engine) is used to recognize the user's emotions and quantify the results.

[1257] Etude generation

[1258] The server then uses these analysis results to run a music generation algorithm (for example, an algorithm built using a Python music generation library) to generate practice pieces. The algorithm creates pieces based on parameters such as pitch, rhythm, range, tempo, and difficulty. It also takes into account the user's emotional state; for example, if the user is sad, it generates a slower song, and if the user is excited, it generates a faster song.

[1259] Providing practice pieces

[1260] The generated practice songs are sent from the server to the terminal, and the terminal provides the user with an interface for playing the received practice songs, allowing the user to practice singing using the provided practice songs.

[1261] Specific examples

[1262] Example 1: Novice user

[1263] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[1264] 2. The device will start recording and stop recording when you finish singing.

[1265] 3. The device sends the recorded data to the server.

[1266] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. Emotion analysis also determines that the user is relaxed.

[1267] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[1268] 6. The server sends the generated practice piece to the device.

[1269] 7. The user practices using the provided practice songs.

[1270] Example 2: Intermediate user

[1271] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[1272] 2. The device will start recording and stop recording when you finish singing.

[1273] 3. The device sends the recorded data to the server.

[1274] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. Emotion analysis also determines that the user is excited.

[1275] 5. The server generates a fast-paced etude with repeated high notes.

[1276] 6. The server sends the generated practice piece to the device.

[1277] 7. The user practices using the provided practice songs.

[1278] This system allows users to effectively practice according to their singing skills and emotional state.

[1279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1280] Step 1:

[1281] The user logs into the system and selects a song to sing.

[1282] Input: Username and password, list of practice songs

[1283] Data processing: User authentication (database matching), acquisition of practice song list

[1284] Output: Authentication success message, practice song list

[1285] Specific operation: The user logs in by entering their username and password on the device. If login is successful, the device retrieves a list of practice songs from the server and displays it to the user. The user then selects the song they want to practice from the displayed list.

[1286] Step 2:

[1287] The device records the user's voice.

[1288] Input: User's singing voice

[1289] Data processing: Digital conversion of audio, high-quality recording, noise canceling processing

[1290] Output: Recorded audio data

[1291] How it works: When a user starts singing along with a selected song, the device will record the sound using a built-in or externally connected microphone. This recording will then be temporarily stored as digital data in storage.

[1292] Step 3:

[1293] The device sends the recorded data to the server.

[1294] Input: Pre-recorded audio data

[1295] Data processing: Reading audio data and preparing it for transmission, encryption via SSL / TLS

[1296] Output: Notification of completion of transmission to the server

[1297] Specific operation: The recorded audio data is read from the storage and sent to the server via the Internet. When sending the data, confidentiality is maintained using secure protocols such as SSL / TLS.

[1298] Step 4:

[1299] The server analyzes the audio data and evaluates singing skill and emotion.

[1300] Input: Audio data

[1301] Data processing: Analysis of pitch, rhythm, range, tempo, emotion recognition and quantification

[1302] Output: Singing skill score and emotion score

[1303] Specific operation: After receiving the voice data, the server inputs the data into a voice analysis engine (e.g., a general voice analysis engine) to perform a detailed analysis of the user's singing skills. After that, it uses an emotion analysis engine to recognize the user's emotional state and quantify each of them.

[1304] Step 5:

[1305] The server generates practice pieces based on the analysis results.

[1306] Input: Singing skill score and emotion score

[1307] Data processing: Execution of music generation algorithms, automatic generation of practice songs

[1308] Output: Generated practice piece data

[1309] How it works: Based on the analysis results, the server runs a music generation algorithm using Python to automatically generate practice pieces optimized for the user's skill and emotions. Parameters such as tempo, range, and difficulty are adjusted to generate songs that also take the user's emotional state into account.

[1310] Step 6:

[1311] The server sends the generated practice piece to the terminal.

[1312] Input: Generated etude data

[1313] Data processing: Reading and sending practice song data, encryption by SSL / TLS

[1314] Output: Notification of completion of transmission to the terminal

[1315] Specific operation: The generated practice piece is temporarily stored and then sent to the device via the Internet. When sending data, confidentiality is maintained using secure protocols such as SSL / TLS.

[1316] Step 7:

[1317] The device plays the practice songs and provides them to the user.

[1318] Input: Received practice piece data

[1319] Data processing: Preparation for playback, display of playback interface

[1320] Output: Practice piece played, user feedback

[1321] Specific operation: The device provides an interface for playing the received practice songs, and the user can practice singing while playing the songs on the device. The interface also has loop playback and speed adjustment functions to support the user's practice experience.

[1322] (Application example 2)

[1323] 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."

[1324] Existing singing practice systems focus only on the user's singing skills and do not take into account their emotional state. This makes it difficult for users to practice in a way that suits their emotions. Furthermore, the practice songs provided to users are not individually customized, making it difficult for users to maintain their growth and motivation. Furthermore, the lack of effective feedback makes it difficult for users to understand where they need to improve.

[1325] The specific processing by the specific 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 means for analyzing the user's voice data and evaluating the singing skill and emotion, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece using a prompt sentence. This allows the user to use a practice piece individually customized according to their singing skill and emotional state, enabling effective practice that helps them maintain motivation.

[1326] A "user" is a person who uses this system to practice singing.

[1327] "Audio data" refers to audio information recorded when a user sings.

[1328] A "server" is a computer system that receives audio data, analyzes it, and generates practice pieces.

[1329] "Evaluation" refers to the use of analytical means to quantify and judge the user's singing skill and emotional state.

[1330] "Etudes" are pieces of music generated according to the user's singing skills and emotions.

[1331] The "analysis means" is a system that has the function of analyzing and quantifying the user's singing skills and emotions from audio data.

[1332] "Emotion" refers to the psychological state of the user while singing, and is quantified by analytical means.

[1333] "Providing" means sending the generated practice piece to the user's device so that the user can practice it.

[1334] A "prompt" is a sentence that instructs the user on the next action or exercise to be taken.

[1335] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. Detailed embodiments of this system will be described below.

[1336] System Overview

[1337] This system records the user's voice data and sends the recorded data to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing effectively.

[1338] Hardware and Software

[1339] Terminal: A device that records and plays audio data and communicates with a server. Examples include smartphones, tablets, and head-mounted displays.

[1340] Server: A computer system that analyzes and evaluates audio data and generates practice pieces. The main software used is a voice analysis engine (e.g., librosa), an emotion recognition engine (e.g., EmotionRecognition), and a practice piece generation engine (e.g., generative AI model).

[1341] Communication method: An internet connection to transfer data between your device and our server.

[1342] Recording and transmitting audio data

[1343] The user uses the device to sing a designated song according to a specific guide, and the device records this voice and transmits the recorded data to a server via the Internet.

[1344] Analysis and evaluation of speech data

[1345] The server inputs the received voice data into a voice analysis engine, which analyzes and quantifies the user's pitch, rhythm, range, and tempo in detail. It also uses an emotion recognition engine to analyze the user's emotional state from the voice data and quantifies this as well.

[1346] Etude generation

[1347] Based on the results of voice analysis and emotion recognition, the server generates practice songs suited to the user. The song is automatically created taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotional state. This allows the server to provide a fast-tempo song if the user is excited, and a slower-tempo song if the user is relaxed.

[1348] Providing practice pieces

[1349] The generated practice pieces are sent from the server to the user's device, which provides an interface for playing the received practice pieces to support the practice. For playback, a library such as Pydub is used.

[1350] Specific use cases

[1351] For novice users

[1352] 1. The device prompts the user to sing a "simple song."

[1353] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[1354] 3. The device sends the recorded data to the server.

[1355] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion recognition engine also determines that the user is relaxed.

[1356] 5. The server generates the appropriate practice piece and sends it to the user's device.

[1357] 6. The user practices using the practice songs provided.

[1358] For intermediate users

[1359] 1. The device prompts the user to sing a "medium difficulty song."

[1360] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[1361] 3. The device sends the recorded data to the server.

[1362] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are unstable. The emotion recognition engine also determines that the user is excited.

[1363] 5. The server generates a fast-paced practice piece with repeated high notes and sends it to the user's device.

[1364] 6. The user practices using the practice songs provided.

[1365] Prompt Sentence Examples

[1366] Design a system that analyzes a user's singing audio data and generates personalized practice songs. Use the following information:

[1367] Records audio data and sends it to the server

[1368] Server-side voice analysis evaluates the user's singing skill and emotional state

[1369] Generates customized practice pieces based on user analysis results

[1370] Provides practice pieces and displays a guiding interface for users

[1371] Examples:

[1372] 1. Beginner users should sing "easy songs."

[1373] 2. Intermediate users sing "songs of medium difficulty."

[1374] In this way, the system can provide effective singing practice according to the user's singing skill and emotions.

[1375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1376] Step 1:

[1377] The user begins singing.

[1378] The device displays a guide message to the user and instructs them to sing a specified song. The input is the user's real-time voice, and the output is a trigger to start recording.

[1379] Step 2:

[1380] The device records the user's singing voice.

[1381] When the user starts singing, the device records the voice data using the built-in microphone. The input is the user's singing voice (analog signal), and the output is the recorded digital voice file (e.g., WAV format).

[1382] Step 3:

[1383] Once the recording is complete, the device sends the audio data to the server.

[1384] When the user finishes singing, the device temporarily stores the audio data and sends it to a server via the Internet. The input is a digital audio file, and the output is a status indicating that data transfer to the server has been completed.

[1385] Step 4:

[1386] The server analyzes the received audio data.

[1387] The server inputs the received audio data into an analysis engine (e.g., librosa), which analyzes and quantifies the pitch, rhythm, range, and tempo in detail. The input is a digital audio file, and the output is quantified singing skill data.

[1388] Step 5:

[1389] The server evaluates the emotion using an emotion recognition engine.

[1390] The server inputs the analyzed voice data into an emotion recognition engine (e.g., EmotionRecognition), which analyzes and quantifies the user's emotional state. The input is quantified singing skill data, and the output is quantified emotional data.

[1391] Step 6:

[1392] The server generates practice songs based on singing skill and emotional state.

[1393] The server provides singing skill data and emotional data as input to the generative AI model, which then generates the optimal practice song for the user. The input is singing skill data and emotional data, and the output is a digital file of the generated practice song.

[1394] Step 7:

[1395] The server transmits the generated practice piece to the terminal.

[1396] The server sends the generated practice piece to the user's device. The input is the digital file of the created practice piece, and the output is the status of the completion of data transfer to the device.

[1397] Step 8:

[1398] The device provides the practice songs to the user and plays them.

[1399] The device displays an interface for playing the received practice piece and prompts the user to practice. The input is the generated digital file of the practice piece, and the output is the user's practice behavior.

[1400] Through the above steps, the user can practice singing effectively using practice songs that are individually customized according to the user's singing skill and emotional state.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] [Fourth embodiment]

[1405] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1406] 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.

[1407] 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).

[1408] 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.

[1409] 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.

[1410] 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).

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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."

[1418] The present invention relates to a system that generates and provides personalized practice songs tailored to a user's singing skills. This system includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[1419] System Overview

[1420] This system first records the user's voice data and sends the recording to a server. The server analyzes the received voice data to evaluate the user's singing skill and generates practice songs based on the evaluation results. The generated practice songs are then provided to the user, allowing them to practice singing efficiently.

[1421] Collecting user voice data

[1422] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[1423] Sending audio data

[1424] The device temporarily stores the recorded audio data and then transmits it to a server via the Internet.

[1425] Analysis of audio data

[1426] The server inputs the received audio data into a voice analysis engine, which analyzes the user's pitch, rhythm, range, tempo, and other parameters in detail to quantify their singing skills, making it clear which areas they are good at and which areas need improvement.

[1427] Etude generation

[1428] Based on the analysis results, the server generates practice songs suited to the user, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty level, and automatically creates songs that encourage the user's growth. For example, songs with minimal fluctuation in scale to stabilize pitch, or songs with many high-pitched parts to strengthen the high range, may be generated.

[1429] Providing practice pieces

[1430] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[1431] Specific examples

[1432] Example 1: Novice user

[1433] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user sings.

[1434] 2. The device will start recording and stop recording when you finish singing.

[1435] 3. The device sends the recorded data to the server.

[1436] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good.

[1437] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[1438] 6. The server sends the generated practice piece to the device.

[1439] 7. The user practices using the provided practice songs.

[1440] Example 2: Intermediate user

[1441] 1. The device prompts the user to sing "Let it Go," and the user sings.

[1442] 2. The device will start recording and stop recording when you finish singing.

[1443] 3. The device sends the recorded data to the server.

[1444] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable.

[1445] 5. The server generates an etude with repeated high notes.

[1446] 6. The server sends the generated practice piece to the device.

[1447] 7. The user practices using the provided practice songs.

[1448] This allows users to effectively practice according to their singing ability, and can expect to improve their skills in a short period of time.

[1449] The processing flow will be explained below.

[1450] Step 1:

[1451] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[1452] Step 2:

[1453] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[1454] Step 3:

[1455] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[1456] Step 4:

[1457] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[1458] Step 5:

[1459] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[1460] Step 6:

[1461] The server inputs the received audio data into a voice analysis engine, which performs detailed analysis of pitch, rhythm, range, and tempo.

[1462] Step 7:

[1463] The server evaluates the user's singing skills based on the analysis results, and the evaluation is quantified and saved in the user's skill profile.

[1464] Step 8:

[1465] The server sets parameters for generating practice songs based on the user's singing skill evaluation, including tempo, note range, and difficulty level.

[1466] Step 9:

[1467] The server creates personalized etudes using an etude generation algorithm that constructs the etude according to set parameters.

[1468] Step 10:

[1469] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[1470] Step 11:

[1471] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[1472] Step 12:

[1473] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[1474] Step 13:

[1475] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[1476] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills.

[1477] Example 1

[1478] 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."

[1479] Conventional singing practice systems have difficulty in providing detailed evaluations of individual users' singing skills and providing appropriate practice songs based on those evaluations, limiting the effectiveness of practice. Furthermore, despite technological advances in audio data analysis and practice song generation, there has been no system that effectively combines these technologies. As a result, users have been unable to efficiently and effectively improve their singing skills.

[1480] 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.

[1481] In this invention, the server includes means for recording the user's voice data, means for transmitting the recorded voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice songs suitable for the user based on the evaluation results, means for providing the generated practice songs to the user, means for analyzing the user's voice data based on elements such as pitch, rhythm, range, and tempo and quantifying the evaluation results, and means for using a generative AI model based on the analysis results to create practice songs that are optimal for the user based on prompt sentences, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. This allows users to practice individually and optimally based on their own singing skill, enabling them to effectively improve their skills in a short period of time.

[1482] A "user" is an individual who uses a recording means to provide audio data, receives practice songs, and practices singing.

[1483] A "terminal" is a device used by a user that records audio, transmits audio data to a server, and plays back practice pieces.

[1484] A "server" is a computing device or system that receives and analyzes audio data, and generates and provides optimal practice pieces to users.

[1485] The "recording means" is a function or device for capturing the user's singing voice and saving it as audio data.

[1486] "Transmission means" refers to a function or device for transmitting recorded audio data to a server via the Internet or other communication means.

[1487] The "analysis means" is a function or system that analyzes the received audio data, evaluates elements such as pitch, rhythm, range, and tempo, and quantifies the user's singing skill.

[1488] The "practice piece generation means" is a function or system that generates a practice piece suitable for the user based on the analysis results.

[1489] The "providing means" is a function or device that transmits the created practice piece to the user's terminal and enables the user to play the practice piece.

[1490] "Pitch" is an element that indicates the pitch of each note while the user is singing.

[1491] "Rhythm" is an element that identifies time elements such as the length of notes and beats while the user is singing.

[1492] "Range" is an element that indicates the range of sounds that a user can produce, from the lowest to the highest note.

[1493] "Tempo" is an element that indicates the speed and slowness of the sound being sung.

[1494] The "analysis results" are data that express the evaluation of pitch, rhythm, range, tempo, etc. obtained by the audio data analysis means in numerical or other formats.

[1495] A "generative AI model" is an artificial intelligence algorithm used to generate optimal practice songs that take into account the user's singing skills.

[1496] A "prompt" is an instruction given to a generative AI model, a document that specifies the characteristics and elements of the practice piece to be generated.

[1497] This clearly defines the function and role of each element included in the claims.

[1498] This invention relates to a system that generates and provides practice songs personalized to a user's singing skill. The system mainly includes a recording means, a transmission means, an analysis means, a practice song generation means, and a provision means. These means work together to provide the user with the practice songs that are most suitable for them. An embodiment of the system is described in detail below.

[1499] Collecting user voice data

[1500] The device has a function for recording the user's singing voice. Specifically, the device is equipped with a microphone function, which the application uses to capture voice data. When the user sings the specified song according to the application's instructions, the voice is recorded.

[1501] example:

[1502] The device will display the instruction "Sing 'Twinkle Twinkle Little Star'" and begin recording.

[1503] The user starts singing and when they finish singing, the recording stops.

[1504] Sending audio data

[1505] The device temporarily stores the recorded audio data and then transmits it to a server. The transmission is via the Internet and the audio data is sent in compressed form. During transmission, checks are performed to ensure the data is intact.

[1506] example:

[1507] The device will display "Recording complete, sending data."

[1508] The terminal compresses the audio data and sends it to the server.

[1509] The server receives the data and returns a confirmation message.

[1510] Analysis of audio data

[1511] The server inputs the received voice data into a voice analysis engine. This engine utilizes a generative AI model to perform detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies the user's singing skill. The results of these analyses are then stored in a database.

[1512] example:

[1513] The server generates a log stating "Analyzing audio data."

[1514] The server divides the audio data into pitch and rhythm and analyzes it.

[1515] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[1516] Etude generation

[1517] Based on the analysis results, the server generates the most suitable practice songs for the user. In this case, a generative AI model is used to automatically create practice songs to improve the user's singing skills, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty. The AI ​​model is given appropriate instructions (prompts).

[1518] example:

[1519] The server generates a log saying "Generating appropriate practice pieces."

[1520] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[1521] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[1522] Providing practice pieces

[1523] The generated practice songs are sent from the server to the user's device, which provides the user with an interface that can play the practice songs, allowing the user to listen to the practice songs and practice singing.

[1524] example:

[1525] The server generates a log stating "Sending practice songs to the device."

[1526] The device will display "New practice song received. Would you like to play it?"

[1527] The user selects play and the device plays the practice piece.

[1528] Example prompt

[1529] Below is an example of a prompt sentence that is input to the generative AI model.

[1530] For novice users:

[1531] "The user sang 'Twinkle Twinkle Little Star' and the audio data was sent to the server. Audio analysis revealed that the pitch was unstable but the rhythm was good. Please generate the best practice song for this user."

[1532] For intermediate users:

[1533] "The user sang 'Let it Go' and the audio data was sent to the server. Audio analysis showed that the pitch, rhythm, and tempo were mostly accurate, but the high notes were a little unstable. Please generate an optimal practice song for this user."

[1534] This allows users to effectively practice according to their singing skills, improving their singing skills in a short period of time.

[1535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1536] Step 1:

[1537] The user follows the application's instructions to sing the specified song, and the device records the audio.

[1538] Specific behavior:

[1539] The device displays the instruction, "Sing 'Twinkle Twinkle Little Star'."

[1540] The user starts singing, and the device uses a microphone to capture the sound and generate audio data.

[1541] Input: User's singing voice

[1542] Output: Recorded audio data

[1543] Step 2:

[1544] The device temporarily stores the recorded audio data and then transmits it over the internet to a server, where checks are performed to ensure data integrity during transmission.

[1545] Specific behavior:

[1546] The device will display "Recording complete, sending data."

[1547] The terminal compresses the audio data and sends it to the server.

[1548] The server receives the data and returns a confirmation message.

[1549] Input: Recorded audio data

[1550] Output: Confirmation message, notification of completion of sending

[1551] Step 3:

[1552] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to perform detailed analysis of pitch, rhythm, range, tempo, and other elements to quantify the user's singing skill.

[1553] Specific behavior:

[1554] The server generates a log stating "Analyzing audio data."

[1555] The server analyzes the audio data, dividing it into pitch and rhythm for analysis.

[1556] The server saves the result as "Analysis complete, skill level is 'Pitch: 65, Rhythm: 80'."

[1557] Input: Received audio data

[1558] Output: Voice analysis results, quantified skill level

[1559] Step 4:

[1560] The server generates practice songs suitable for the user based on the results of audio analysis, using a generative AI model to create the optimal practice songs, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty.

[1561] Specific behavior:

[1562] The server generates a log saying "Generating appropriate practice pieces."

[1563] The server inputs a prompt to the AI ​​model saying, "The pitch is unstable, so please generate a song with less fluctuation."

[1564] The server saves the result with the message "Generation completed, practice song 'Easy Melody' saved."

[1565] Input: Voice analysis results, prompt text

[1566] Output: Generated exercises

[1567] Step 5:

[1568] The server sends the created practice piece to the terminal, which provides the user with an interface that can play the practice piece, allowing the user to listen to the practice piece and practice singing.

[1569] Specific behavior:

[1570] The server generates a log stating "Sending practice songs to the device."

[1571] The device will display "New practice song received. Would you like to play it?"

[1572] The user selects play and the device plays the practice piece.

[1573] Input: Generated Etude

[1574] Output: Practice song to play, user starts singing practice

[1575] By clarifying the specific operations performed at each step and the input / output flow in this way, it becomes easier to understand the processing flow of the entire system and the role of each step.

[1576] (Application example 1)

[1577] 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."

[1578] In recent years, the importance of singing practice tailored to individual needs has increased in the field of music education. However, conventional singing practice systems often lack the functionality to automatically generate and provide appropriate practice songs based on the user's singing skill. They also lack a mechanism to visualize the user's progress and provide specific feedback. This has made it difficult to provide efficient and effective practice.

[1579] 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.

[1580] In this invention, the server includes means for recording the user's voice data, means for transmitting the voice data to the server, means for analyzing the voice data and evaluating the user's singing skill, means for generating practice pieces suitable for the user based on the evaluation results, means for providing the generated practice pieces to the user, means for managing the progress of the user's singing skill based on the analysis results and visualizing the progress, and means for providing specific advice based on the practice results. This not only makes it possible to provide practice pieces that meet the individual needs of the user, but also makes it possible to visualize the user's progress and provide objective and specific feedback.

[1581] "User" refers to an individual who uses this system to practice singing.

[1582] "Audio data" refers to data containing information about audio signals recorded when a user sings.

[1583] "Server" refers to a data center or computing infrastructure for receiving and analyzing audio data, and generating and providing practice pieces.

[1584] "Recording means" means any device or software used to digitally record a user's singing voice.

[1585] "Transmitting means" refers to a network communication means for transferring recorded audio data to a server.

[1586] "Means of analysis" refers to algorithms and software that use an audio analysis engine to perform detailed analysis of pitch, rhythm, range, tempo, etc.

[1587] "Evaluation" refers to quantifying a user's singing skills based on the analysis results.

[1588] "Means for generating" refers to a program or algorithm that automatically creates practice pieces suitable for the user based on the analysis results.

[1589] "Means of providing" refers to the mechanism for sending the generated practice piece to the user's device and making it playable.

[1590] "Progress management tools" refers to systems and features that record and visualize the user's progress in singing skills.

[1591] "Specific advice" refers to feedback such as improvements and recommendations provided to users based on their practice results.

[1592] The present invention relates to a system for generating and providing practice songs personalized to a user's singing skills, including a recording means, a transmitting means, a analyzing means, a evaluating means, a generating means, a providing means, a progress management means, and a means for providing specific advice.

[1593] System Overview

[1594] The entire system starts by recording the user's voice data and sending it to the server. The server receives the voice data and analyzes it using a voice analysis engine. Based on the analysis results, the server evaluates the user's singing skills and generates personalized practice songs according to the evaluation results. The practice songs are then provided to the user's device, which further visualizes the user's progress and provides specific advice.

[1595] Recording audio data

[1596] Users record their singing using a device (smartphone, smart glasses, head-mounted display, etc.), and the recording data is digitally recorded and temporarily stored on the device.

[1597] Sending audio data

[1598] The recorded audio data is transmitted to a server via the Internet using an appropriate network communication means.

[1599] Analysis of audio data

[1600] The server analyzes the received audio data and uses an audio analysis engine (such as librosa or a similar audio analysis library) to perform detailed analysis of the user's pitch, rhythm, range, tempo, and other characteristics, converting them into numerical values.

[1601] Singing skill evaluation

[1602] Based on the analysis, the server evaluates the user's singing skills based on a number of parameters, including pitch stability, rhythmic accuracy, range, and tempo consistency.

[1603] Etude generation

[1604] Based on the evaluation results, the server generates an optimal practice piece for the user, with parameters such as tempo, note range, and difficulty set according to the user's skill level.

[1605] Providing practice pieces

[1606] The generated practice piece is sent from the server to the user's device, which then provides the received practice piece to the user in a playable format.

[1607] Progress management and advice provision

[1608] The server records the user's practice history, visualizes their progress, and provides specific advice based on their practice results, such as "Practice your high-pitched notes more."

[1609] Specific examples

[1610] For example, if a user sings "Twinkle Twinkle Little Star" on their smartphone, the recording is sent to the server for analysis. If the analysis determines that the user's pitch is unstable, a practice song with a slower tempo and less variation in the scale is generated and provided to the user. The user can then practice with the provided practice song to improve their skills.

[1611] Prompt Sentence Examples

[1612] example:

[1613] Analyze the user's audio data (example.wav) and evaluate its tempo, pitch, rhythm, etc. Then, based on the results of this evaluation, generate an optimal practice piece for the user. The generated practice piece should be designed as a simple piece for beginners, with a slow tempo and minimal variation in the scale.

[1614] This example allows the system to provide specific and effective personalized practice songs to help users improve their singing skills.

[1615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1616] Step 1:

[1617] The user uses the device to record their singing voice. Specifically, they launch the recording application and start singing a designated song, and the audio data is recorded in digital format through the device's built-in microphone. The input is the user's singing voice, and the output is digital audio data.

[1618] Step 2:

[1619] The device temporarily stores the recorded audio data and transmits it to a server via the Internet using a data transfer protocol (e.g. HTTP / HTTPS). The input is digital audio data, and the output is data transmission to the server.

[1620] Step 3:

[1621] The server analyzes the received audio data. Using an audio analysis engine (e.g., librosa library), it performs detailed analysis of characteristics such as pitch, rhythm, range, and tempo. The input is digital audio data, and the output is numerical data containing the analysis results.

[1622] Step 4:

[1623] The server evaluates the user's singing skill based on the analysis results, using parameters such as pitch stability, rhythm accuracy, range, and tempo consistency. The input is numerical data containing the analysis results, and the output is evaluation data that evaluates the user's singing skill.

[1624] Step 5:

[1625] The server generates the most suitable practice pieces for the user based on the evaluation results. It uses a generative AI model to automatically create practice pieces that fit the evaluation data. The input is the evaluation data, and the output is personalized practice piece data.

[1626] Step 6:

[1627] The server then sends the generated exercise to the user's device, converting the exercise data into an appropriate format (e.g., MP3 file) and transmitting it over the Internet. The input is the personalized exercise data, and the output is the transmission of the exercise to the user's device.

[1628] Step 7:

[1629] The user's device plays the received practice songs, and the user practices singing using them. Specifically, the media player function for playing practice songs is used. The input is the practice song data, and the output is audio playback.

[1630] Step 8:

[1631] The server records the user's practice history and stores and analyzes the data to visualize their progress. This allows them to compare their progress with past practice data. The input is the user's practice history data, and the output is the visualized progress data.

[1632] Step 9:

[1633] The server provides specific advice to the user based on the practice results. For example, it generates feedback such as "Practice more on the high-pitched parts" based on the analysis results and sends it to the user. The input is the practice result data, and the output is a specific advice message.

[1634] 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.

[1635] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[1636] System Overview

[1637] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[1638] Collecting user voice data

[1639] The device has a function for recording the user's singing voice. When the user sings a specified song following the system's guide, the voice is recorded.

[1640] Sending audio data

[1641] The device temporarily stores the recorded audio data in its storage, and then transmits the data to a server via the Internet.

[1642] Analysis of audio data

[1643] The server inputs the received voice data into a voice analysis engine. This engine performs detailed analysis of the user's pitch, rhythm, range, tempo, etc., and quantifies their singing skills. This makes it clear which areas they are good at and which areas need improvement. The emotion engine also recognizes the user's emotions from the voice data and quantifies them.

[1644] Etude generation

[1645] The server generates practice songs suited to the user based on the results of voice analysis and emotion recognition. It automatically creates songs that encourage the user's growth by taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotions. For example, if the user seems sad, it will generate a song with a slow tempo, and if they seem excited, it will generate a song with a fast tempo.

[1646] Providing practice pieces

[1647] The generated practice songs are sent from the server to the user's device, which provides the user with an interface for playing the received practice songs and supports singing practice.

[1648] Specific examples

[1649] Example 1: Novice user

[1650] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[1651] 2. The device will start recording and stop recording when you finish singing.

[1652] 3. The device sends the recorded data to the server.

[1653] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion engine also determines that the user is relaxed.

[1654] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[1655] 6. The server sends the generated practice piece to the device.

[1656] 7. The user practices using the provided practice songs.

[1657] Example 2: Intermediate user

[1658] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[1659] 2. The device will start recording and stop recording when you finish singing.

[1660] 3. The device sends the recorded data to the server.

[1661] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. The emotion engine also determines that the user is excited.

[1662] 5. The server generates a fast-paced etude with repeated high notes.

[1663] 6. The server sends the generated practice piece to the device.

[1664] 7. The user practices using the provided practice songs.

[1665] This allows users to effectively practice according to their singing skills and emotional state.

[1666] The processing flow will be explained below.

[1667] Step 1:

[1668] The device displays the singing test start screen. Instructions are displayed for the user to start singing. The user presses the start button to perform the singing test presented by the system.

[1669] Step 2:

[1670] The device will record the voice when the user starts singing, and during the recording, the device will save the voice data in real time.

[1671] Step 3:

[1672] The user completes the singing test provided, and when the singing test is finished, the user presses the stop button to end the recording.

[1673] Step 4:

[1674] The device temporarily stores the recorded audio data in its storage, which is then converted into an audio file format.

[1675] Step 5:

[1676] The device then sends the saved audio file to a server over the internet using a secure communication protocol.

[1677] Step 6:

[1678] The server inputs the received audio data into a voice analysis engine, which analyzes the pitch, rhythm, range, and tempo in detail and generates numerical evaluation data.

[1679] Step 7:

[1680] The server also inputs the voice data into the emotion engine, which recognizes the user's emotion based on features extracted from the voice and generates emotion evaluation data.

[1681] Step 8:

[1682] The server combines data from both the voice analysis engine and the emotion engine to evaluate the user's singing skill and emotional state, and stores the results of these evaluations as a numerical value in the user profile.

[1683] Step 9:

[1684] Based on the evaluation results, the server sets parameters to generate practice pieces suited to the user, including tempo, range of notes, difficulty level, and emotional state.

[1685] Step 10:

[1686] The server uses a practice song generation algorithm to create practice songs according to the set parameters, such as songs for stabilizing pitch or songs with tempo adjustments based on the user's emotional state.

[1687] Step 11:

[1688] The server converts the generated practice pieces into music file formats, which are then ready to be distributed to users.

[1689] Step 12:

[1690] The server then sends the music files of the practice pieces to the devices, which are then transmitted over the internet using secure communication protocols.

[1691] Step 13:

[1692] The device displays an interface for playing the received practice song, and the user can begin practicing singing the provided practice song through this interface.

[1693] Step 14:

[1694] Users practice singing using the provided practice songs, and the device offers the option to record their practice progress, allowing users to track their progress.

[1695] In this way, specific processing is performed for each step, allowing the user to practice efficiently with practice songs that suit their singing skills and emotions.

[1696] Example 2

[1697] 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."

[1698] Conventional singing practice systems have been unable to provide practice songs that take into account not only the user's singing skill but also their emotional and psychological state. As a result, users are unable to practice effectively to improve their singing skills, and it is difficult to provide a practice experience that suits each individual's emotional state. There is also a need for a system that can clearly identify the user's specific weaknesses and challenges and generate practice songs that are suited to them.

[1699] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1700] In this invention, the server includes means for analyzing the voice data and evaluating the user's singing skill and emotions, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece to the user, thereby enabling the provision of practice pieces that take into account both the user's singing skill and emotional state.

[1701] "User's voice data" refers to voice information sung by a user, and is digital data collected by a recording device.

[1702] "Recording means" refers to devices or software that record audio as digital data.

[1703] "Electronic computer" refers to a computer or server that has the ability to receive, process, store, and analyze voice data.

[1704] "Means for analyzing" refers to software or algorithms for analyzing digitized audio data and extracting specific parameters.

[1705] "Singing skill" is a numerical representation of a user's musical ability, such as pitch, rhythm, range, and tempo.

[1706] "Emotion" refers to the psychological state and mood that the user has when singing, and is information analyzed from audio data.

[1707] "Means of evaluation" refers to the process of quantifying a user's singing skills and emotions based on analyzed audio data.

[1708] "Means for generating practice songs" refers to algorithms or software that automatically generate new practice songs based on the user's singing skill and emotions.

[1709] "Means provided to the user" refers to an interface or application that allows the user to play the generated practice piece.

[1710] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. This system includes a recording means, a transmission means, an analysis means, an emotion engine, a practice song generation means, and a provision means, and these means work together to provide the user with the most suitable practice songs.

[1711] This system records the user's voice and sends the recording to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing efficiently.

[1712] Collecting user voice data

[1713] The device uses a built-in or externally connected microphone to record the user's singing voice. When the user sings the designated song following the system's instructions, the voice is recorded and saved as high-quality digital data.

[1714] Sending audio data

[1715] The device reads the recorded audio data from the storage and transmits it to the server over the Internet, using secure protocols such as SSL / TLS to protect the confidentiality and integrity of the data.

[1716] Analysis of audio data

[1717] The server inputs the received voice data into an analysis engine (e.g., a general voice analysis engine). This engine performs detailed analysis of the user's skill elements, such as pitch, rhythm, range, and tempo, and generates a skill score. For emotion analysis, a separate emotion engine (e.g., a general emotion analysis engine) is used to recognize the user's emotions and quantify the results.

[1718] Etude generation

[1719] The server then uses these analysis results to run a music generation algorithm (for example, an algorithm built using a Python music generation library) to generate practice pieces. The algorithm creates pieces based on parameters such as pitch, rhythm, range, tempo, and difficulty. It also takes into account the user's emotional state; for example, if the user is sad, it generates a slower song, and if the user is excited, it generates a faster song.

[1720] Providing practice pieces

[1721] The generated practice songs are sent from the server to the terminal, and the terminal provides the user with an interface for playing the received practice songs, allowing the user to practice singing using the provided practice songs.

[1722] Specific examples

[1723] Example 1: Novice user

[1724] 1. The device prompts the user to sing "Twinkle Twinkle Little Star," and the user follows the instructions.

[1725] 2. The device will start recording and stop recording when you finish singing.

[1726] 3. The device sends the recorded data to the server.

[1727] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. Emotion analysis also determines that the user is relaxed.

[1728] 5. The server generates practice pieces with a slow tempo and minimal scale variation.

[1729] 6. The server sends the generated practice piece to the device.

[1730] 7. The user practices using the provided practice songs.

[1731] Example 2: Intermediate user

[1732] 1. The device prompts the user to sing "Let it Go," and the user follows the instructions.

[1733] 2. The device will start recording and stop recording when you finish singing.

[1734] 3. The device sends the recorded data to the server.

[1735] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are a little unstable. Emotion analysis also determines that the user is excited.

[1736] 5. The server generates a fast-paced etude with repeated high notes.

[1737] 6. The server sends the generated practice piece to the device.

[1738] 7. The user practices using the provided practice songs.

[1739] This system allows users to effectively practice according to their singing skills and emotional state.

[1740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1741] Step 1:

[1742] The user logs into the system and selects a song to sing.

[1743] Input: Username and password, list of practice songs

[1744] Data processing: User authentication (database matching), acquisition of practice song list

[1745] Output: Authentication success message, practice song list

[1746] Specific operation: The user logs in by entering their username and password on the device. If login is successful, the device retrieves a list of practice songs from the server and displays it to the user. The user then selects the song they want to practice from the displayed list.

[1747] Step 2:

[1748] The device records the user's voice.

[1749] Input: User's singing voice

[1750] Data processing: Digital conversion of audio, high-quality recording, noise canceling processing

[1751] Output: Recorded audio data

[1752] How it works: When a user starts singing along with a selected song, the device will record the sound using a built-in or externally connected microphone. This recording will then be temporarily stored as digital data in storage.

[1753] Step 3:

[1754] The device sends the recorded data to the server.

[1755] Input: Pre-recorded audio data

[1756] Data processing: Reading audio data and preparing it for transmission, encryption via SSL / TLS

[1757] Output: Notification of completion of transmission to the server

[1758] Specific operation: The recorded audio data is read from the storage and sent to the server via the Internet. When sending the data, confidentiality is maintained using secure protocols such as SSL / TLS.

[1759] Step 4:

[1760] The server analyzes the audio data and evaluates singing skill and emotion.

[1761] Input: Audio data

[1762] Data processing: Analysis of pitch, rhythm, range, tempo, emotion recognition and quantification

[1763] Output: Singing skill score and emotion score

[1764] Specific operation: After receiving the voice data, the server inputs the data into a voice analysis engine (e.g., a general voice analysis engine) to perform a detailed analysis of the user's singing skills. After that, it uses an emotion analysis engine to recognize the user's emotional state and quantify each of them.

[1765] Step 5:

[1766] The server generates practice pieces based on the analysis results.

[1767] Input: Singing skill score and emotion score

[1768] Data processing: Execution of music generation algorithms, automatic generation of practice songs

[1769] Output: Generated practice piece data

[1770] How it works: Based on the analysis results, the server runs a music generation algorithm using Python to automatically generate practice pieces optimized for the user's skill and emotions. Parameters such as tempo, range, and difficulty are adjusted to generate songs that also take the user's emotional state into account.

[1771] Step 6:

[1772] The server sends the generated practice piece to the terminal.

[1773] Input: Generated etude data

[1774] Data processing: Reading and sending practice song data, encryption by SSL / TLS

[1775] Output: Notification of completion of transmission to the terminal

[1776] Specific operation: The generated practice piece is temporarily stored and then sent to the device via the Internet. When sending data, confidentiality is maintained using secure protocols such as SSL / TLS.

[1777] Step 7:

[1778] The device plays the practice songs and provides them to the user.

[1779] Input: Received practice piece data

[1780] Data processing: Preparation for playback, display of playback interface

[1781] Output: Practice piece played, user feedback

[1782] Specific operation: The device provides an interface for playing the received practice songs, and the user can practice singing while playing the songs on the device. The interface also has loop playback and speed adjustment functions to support the user's practice experience.

[1783] (Application example 2)

[1784] 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."

[1785] Existing singing practice systems focus only on the user's singing skills and do not take into account their emotional state. This makes it difficult for users to practice in a way that suits their emotions. Furthermore, the practice songs provided to users are not individually customized, making it difficult for users to maintain their growth and motivation. Furthermore, the lack of effective feedback makes it difficult for users to understand where they need to improve.

[1786] The specific processing by the specific 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 means for analyzing the user's voice data and evaluating the singing skill and emotion, means for generating a practice piece suitable for the user based on the evaluation results, and means for providing the generated practice piece using a prompt sentence. This allows the user to use a practice piece individually customized according to their singing skill and emotional state, enabling effective practice that helps them maintain motivation.

[1787] A "user" is a person who uses this system to practice singing.

[1788] "Audio data" refers to audio information recorded when a user sings.

[1789] A "server" is a computer system that receives audio data, analyzes it, and generates practice pieces.

[1790] "Evaluation" refers to the use of analytical means to quantify and judge the user's singing skill and emotional state.

[1791] "Etudes" are pieces of music generated according to the user's singing skills and emotions.

[1792] The "analysis means" is a system that has the function of analyzing and quantifying the user's singing skills and emotions from audio data.

[1793] "Emotion" refers to the psychological state of the user while singing, and is quantified by analytical means.

[1794] "Providing" means sending the generated practice piece to the user's device so that the user can practice it.

[1795] A "prompt" is a sentence that instructs the user on the next action or exercise to be taken.

[1796] The present invention relates to a system that analyzes a user's singing skill and emotions, and generates and provides personalized practice songs based on the analysis. Detailed embodiments of this system will be described below.

[1797] System Overview

[1798] This system records the user's voice data and sends the recorded data to a server. The server analyzes the received voice data and evaluates the user's singing skill and emotion. Based on the evaluation results, practice songs are generated and provided to the user, allowing the user to practice singing effectively.

[1799] Hardware and Software

[1800] Terminal: A device that records and plays audio data and communicates with a server. Examples include smartphones, tablets, and head-mounted displays.

[1801] Server: A computer system that analyzes and evaluates audio data and generates practice pieces. The main software used is a voice analysis engine (e.g., librosa), an emotion recognition engine (e.g., EmotionRecognition), and a practice piece generation engine (e.g., generative AI model).

[1802] Communication method: An internet connection to transfer data between your device and our server.

[1803] Recording and transmitting audio data

[1804] The user uses the device to sing a designated song according to a specific guide, and the device records this voice and transmits the recorded data to a server via the Internet.

[1805] Analysis and evaluation of speech data

[1806] The server inputs the received voice data into a voice analysis engine, which analyzes and quantifies the user's pitch, rhythm, range, and tempo in detail. It also uses an emotion recognition engine to analyze the user's emotional state from the voice data and quantifies this as well.

[1807] Etude generation

[1808] Based on the results of voice analysis and emotion recognition, the server generates practice songs suited to the user. The song is automatically created taking into account parameters such as pitch, rhythm, range, tempo, and difficulty, as well as the user's emotional state. This allows the server to provide a fast-tempo song if the user is excited, and a slower-tempo song if the user is relaxed.

[1809] Providing practice pieces

[1810] The generated practice pieces are sent from the server to the user's device, which provides an interface for playing the received practice pieces to support the practice. For playback, a library such as Pydub is used.

[1811] Specific use cases

[1812] For novice users

[1813] 1. The device prompts the user to sing a "simple song."

[1814] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[1815] 3. The device sends the recorded data to the server.

[1816] 4. The server analyzes the audio and determines that the pitch is unstable but the rhythm is good. The emotion recognition engine also determines that the user is relaxed.

[1817] 5. The server generates the appropriate practice piece and sends it to the user's device.

[1818] 6. The user practices using the practice songs provided.

[1819] For intermediate users

[1820] 1. The device prompts the user to sing a "medium difficulty song."

[1821] 2. When the user sings along with the guide, the device starts recording and stops recording when the user finishes singing.

[1822] 3. The device sends the recorded data to the server.

[1823] 4. The server analyzes the audio and determines that the pitch, rhythm, and tempo are mostly accurate, but the high frequencies are unstable. The emotion recognition engine also determines that the user is excited.

[1824] 5. The server generates a fast-paced practice piece with repeated high notes and sends it to the user's device.

[1825] 6. The user practices using the practice songs provided.

[1826] Prompt Sentence Examples

[1827] Design a system that analyzes a user's singing audio data and generates personalized practice songs. Use the following information:

[1828] Records audio data and sends it to the server

[1829] Server-side voice analysis evaluates the user's singing skill and emotional state

[1830] Generates customized practice pieces based on user analysis results

[1831] Provides practice pieces and displays a guiding interface for users

[1832] Examples:

[1833] 1. Beginner users should sing "easy songs."

[1834] 2. Intermediate users sing "songs of medium difficulty."

[1835] In this way, the system can provide effective singing practice according to the user's singing skill and emotions.

[1836] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1837] Step 1:

[1838] The user begins singing.

[1839] The device displays a guide message to the user and instructs them to sing a specified song. The input is the user's real-time voice, and the output is a trigger to start recording.

[1840] Step 2:

[1841] The device records the user's singing voice.

[1842] When the user starts singing, the device records the voice data using the built-in microphone. The input is the user's singing voice (analog signal), and the output is the recorded digital voice file (e.g., WAV format).

[1843] Step 3:

[1844] Once the recording is complete, the device sends the audio data to the server.

[1845] When the user finishes singing, the device temporarily stores the audio data and sends it to a server via the Internet. The input is a digital audio file, and the output is a status indicating that data transfer to the server has been completed.

[1846] Step 4:

[1847] The server analyzes the received audio data.

[1848] The server inputs the received audio data into an analysis engine (e.g., librosa), which analyzes and quantifies the pitch, rhythm, range, and tempo in detail. The input is a digital audio file, and the output is quantified singing skill data.

[1849] Step 5:

[1850] The server evaluates the emotion using an emotion recognition engine.

[1851] The server inputs the analyzed voice data into an emotion recognition engine (e.g., EmotionRecognition), which analyzes and quantifies the user's emotional state. The input is quantified singing skill data, and the output is quantified emotional data.

[1852] Step 6:

[1853] The server generates practice songs based on singing skill and emotional state.

[1854] The server provides singing skill data and emotional data as input to the generative AI model, which then generates the optimal practice song for the user. The input is singing skill data and emotional data, and the output is a digital file of the generated practice song.

[1855] Step 7:

[1856] The server transmits the generated practice piece to the terminal.

[1857] The server sends the generated practice piece to the user's device. The input is the digital file of the created practice piece, and the output is the status of the completion of data transfer to the device.

[1858] Step 8:

[1859] The device provides the practice songs to the user and plays them.

[1860] The device displays an interface for playing the received practice piece and prompts the user to practice. The input is the generated digital file of the practice piece, and the output is the user's practice behavior.

[1861] Through the above steps, the user can practice singing effectively using practice songs that are individually customized according to the user's singing skill and emotional state.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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).

[1869] 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.

[1870] 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."

[1871] 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.

[1872] 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).

[1873] 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.

[1874] 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.

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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.

[1882] 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.

[1883] The following is further disclosed regarding the above embodiment.

[1884] (Claim 1)

[1885] means for recording user voice data;

[1886] means for transmitting the recorded voice data to a server;

[1887] A means for analyzing the audio data and evaluating the user's singing skill;

[1888] a means for generating a practice piece suitable for the user based on the evaluation results;

[1889] The system includes a means for providing the generated practice piece to a user.

[1890] (Claim 2)

[1891] 2. The system according to claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, and tempo.

[1892] (Claim 3)

[1893] 2. The system of claim 1, wherein the practice piece generating means includes means for setting parameters of tempo, range, and difficulty based on the user's singing skill.

[1894] "Example 1"

[1895] (Claim 1)

[1896] means for recording user voice data;

[1897] means for transmitting the recorded voice data to a server;

[1898] A means for analyzing the audio data and evaluating the user's singing skill;

[1899] a means for generating a practice piece suitable for the user based on the evaluation results;

[1900] a means for providing the generated practice piece to a user;

[1901] A method for analyzing the user's voice data based on factors such as pitch, rhythm, range, and tempo, and quantifying the evaluation results.

[1902] The system includes a means for using a generative AI model based on the analysis results to create an optimal practice piece for the user based on a prompt sentence, taking into account parameters such as pitch, rhythm, range, tempo, and difficulty.

[1903] (Claim 2)

[1904] 2. The system according to claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, and tempo.

[1905] (Claim 3)

[1906] The system of claim 1, wherein the practice piece generation means includes means for setting parameters of tempo, note range, and difficulty based on the user's singing skill and generating practice pieces using a generative AI model.

[1907] "Application Example 1"

[1908] (Claim 1)

[1909] means for recording user voice data;

[1910] means for transmitting the recorded voice data to a server;

[1911] A means for analyzing the audio data and evaluating the user's singing skill;

[1912] a means for generating a practice piece suitable for the user based on the evaluation results;

[1913] a means for providing the generated practice piece to a user;

[1914] Based on the analysis results, we will manage the progress of users' singing skills and visualize their progress.

[1915] A system that includes a means to provide specific advice based on the results of practice.

[1916] (Claim 2)

[1917] 2. The system according to claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, and tempo.

[1918] (Claim 3)

[1919] 2. The system of claim 1, wherein the practice piece generating means includes means for setting parameters of tempo, range, and difficulty based on the user's singing skill.

[1920] "Example 2: Combining Emotion Engines"

[1921] (Claim 1)

[1922] means for recording user voice data;

[1923] means for transmitting the recorded voice data to a computer;

[1924] a means for analyzing the audio data and assessing the user's singing skill and emotion;

[1925] a means for generating a practice piece suitable for the user based on the evaluation results;

[1926] The system includes a means for providing the generated practice piece to a user.

[1927] (Claim 2)

[1928] 2. The system of claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, tempo, and emotion.

[1929] (Claim 3)

[1930] 2. The system of claim 1, wherein the practice piece generating means includes means for setting parameters of tempo, range, and difficulty based on the user's singing skill and emotion.

[1931] "Application example 2 when combining emotion engines"

[1932] (Claim 1)

[1933] means for recording user voice data;

[1934] means for transmitting the recorded voice data to a server;

[1935] means for analyzing the audio data to assess the user's singing skill and emotions;

[1936] a means for generating a practice piece suitable for the user based on the evaluation results;

[1937] The system includes a means for providing the generated practice piece to a user.

[1938] (Claim 2)

[1939] 2. The system of claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the user's pitch, rhythm, range, tempo, and emotion.

[1940] (Claim 3)

[1941] 2. The system of claim 1, wherein the practice song generating means includes means for setting parameters of tempo, range, and difficulty based on the user's singing skill and emotion, and for providing the generated song using prompt sentences. [Explanation of symbols]

[1942] 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. means for recording user voice data; means for transmitting the recorded voice data to a server; A means for analyzing the audio data and evaluating the user's singing skill; a means for generating a practice piece suitable for the user based on the evaluation results; The system includes a means for providing the generated practice piece to a user.

2. 2. The system according to claim 1, wherein the voice data analysis means includes means for analyzing and quantifying the pitch, rhythm, range, and tempo of the user.

3. 2. The system of claim 1, wherein the practice piece generating means includes means for setting parameters of tempo, range, and difficulty based on the user's singing skill.

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