system

Abstract

We provide a system that improves the experience for users who cannot enjoy karaoke because they are tone-deaf. [Solution] The system comprises means for acquiring audio data, means for converting the acquired audio data into a digital signal, means for analyzing the digital signal to extract pitch and tempo, means for calculating correction values ​​for correcting the pitch and tempo, means for correcting the audio using the correction values, and means for outputting the corrected audio.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to solve the problem that there are people who hesitate to sing in karaoke due to pitch or tempo deviation. In particular, it is required to solve the problem that karaoke cannot be enjoyed by users with little music experience or that they hesitate to sing with others. It is an object of the present invention to improve the experience of users who cannot enjoy karaoke because they are tone-deaf.

Means for Solving the Problems

[0005] This technology acquires audio data and converts it into a digital signal to analyze pitch and tempo. Based on this analysis, it employs a method to calculate correction values ​​using an AI model trained on past singing data and correct the audio in real time. This system allows users to sing with accurate pitch and tempo, and to listen to the audio output through the speakers comfortably. This invention provides a means to improve the user's karaoke experience by automating pitch deviation and key adjustments.

[0006] "Audio data" refers to information that represents audio signals in digital format.

[0007] A "digital signal" is a signal that numerically represents an analog audio signal and is in a format that can be processed by a computer.

[0008] "Pitch" refers to the element that represents the height of a sound, and is a fundamental unit that makes up the melody of a piece of music.

[0009] "Tempo" refers to the speed at which a piece of music is played and is a factor that determines the rhythm of the music.

[0010] A "correction value" is a calculated value used to effectively correct deviations in pitch and tempo in audio data.

[0011] "Analysis" is the act of analyzing audio data in order to extract pitch and tempo from digital signals.

[0012] "Past singing data" refers to previously recorded singing voice data, which serves as the foundational information for the AI ​​model to learn voice correction.

[0013] An "AI model" is a computational model that uses artificial intelligence to perform pitch and tempo corrections based on singing data.

[0014] "Real-time" means that data processing and audio output occur almost simultaneously with user actions.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The karaoke support system of the present invention improves the user's singing experience using speech recognition technology and speech correction technology.

[0037] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice from the microphone in real time and converts the audio into a digital signal. This audio data is then transmitted to a server via the network.

[0038] The server analyzes the received digital signal and extracts pitch and tempo from the audio. Next, it uses an AI model trained on past singing data to calculate the necessary correction values ​​from the extracted audio features. The server uses these correction values ​​to correct pitch deviations and tempo mismatches in real time. In particular, for pitch correction, it utilizes pitch shifting technology and formant adjustment to correct the voice quality without compromising it.

[0039] The corrected audio data is sent back to the device, which then outputs the audio through its speaker. This process allows the user and surrounding audience to enjoy a more harmonious and pleasant singing voice.

[0040] For example, when a user selects a children's song and begins to sing, if the pitch is off by a semitone, the server detects the discrepancy and calculates a correction value. By correcting the audio based on this value, the final sound output from the speaker will sound as if it were sung in the correct pitch. In this way, the system makes it possible to overcome tone-deafness and enjoy karaoke more.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone.

[0044] Step 2:

[0045] The device acquires the user's singing voice through the microphone and converts that analog signal into a digital signal.

[0046] Step 3:

[0047] The terminal transmits digital audio data acquired in real time to the server via the network.

[0048] Step 4:

[0049] The server applies a Short-Time Fourier Transform (STFT) to the received audio data to analyze and extract pitch and tempo.

[0050] Step 5:

[0051] The server inputs the analysis results into the AI ​​model and calculates correction values ​​using the model, which has been trained based on past singing data.

[0052] Step 6:

[0053] The server uses pitch shifting technology to correct the pitch of the user's voice and maintains voice quality by adjusting formants as needed.

[0054] Step 7:

[0055] The server adjusts for tempo discrepancies, generates corrected audio data at the appropriate timing, and sends it to the terminal.

[0056] Step 8:

[0057] The device outputs the corrected audio data in real time through its speaker, allowing the user and those around them to hear harmonious audio.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In audio recording and broadcasting, inaccurate pitch and tempo can create an unpleasant impression on listeners. This is especially true in situations requiring real-time audio output, such as karaoke, where correcting audio errors immediately is difficult. Therefore, there is a need to instantly correct pitch and tempo discrepancies to provide more natural-sounding audio.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for converting audio data into a digital signal, means for calculating correction values ​​for correcting pitch and tempo using a generation AI model, and means for correcting the audio by performing pitch shifting and formant adjustment. This makes it possible to provide more harmonious audio by correcting pitch and tempo errors in real time.

[0063] "Audio data" refers to information that is composed of sound in digital format and is used to represent acoustic signals.

[0064] A "digital signal" is a data format obtained by sampling and quantizing analog information, and is primarily suited for processing by electronic computers.

[0065] "Interval" refers to a characteristic that indicates the height of a sound, and it forms the basis of melody and chords in music.

[0066] "Tempo" refers to the speed of music and is a factor that determines the pace at which a song progresses.

[0067] A "correction value" is a numerical value calculated to correct errors or inconsistencies, and is used in audio processing to adjust pitch and tempo.

[0068] "Pitch shifting technology" is a technique that changes the pitch of an audio signal and is used to generate sounds of different pitches while maintaining sound quality.

[0069] "Formant adjustment" is a technique for changing the pitch of a voice without altering its quality, and is an acoustic processing method that maintains a natural-sounding voice.

[0070] A "generative AI model" is an artificial intelligence algorithm that uses learning based on past data to perform predictions and classifications.

[0071] The karaoke support system of the present invention uses speech recognition technology and speech correction technology to improve the user's singing experience. Specifically, it enables high-level real-time processing of the voice as the user sings on the karaoke machine.

[0072] The user selects a song on the karaoke machine and begins singing. The terminal captures the user's voice input from the microphone and converts the analog signal into a digital signal. This conversion needs to be highly accurate, and dedicated audio processing software is typically used. The terminal then transmits this digital audio data to a server via the network.

[0073] The server analyzes the received digital audio signal and extracts pitch and tempo from the audio. The extracted data is input into a generative AI model, and the AI, which has learned from past singing data, calculates correction values. Based on these correction values, the server corrects pitch deviations and tempo mismatches using pitch shifting technology and formant adjustment. Through this process, the corrected audio data is provided in real time as a more harmonious and natural-sounding voice.

[0074] The corrected audio data is sent back to the device, which then uses that data to output the corrected audio through its speakers. This technology allows users to check their own singing voice in real time while enjoying a comfortable music experience with those around them.

[0075] For example, if a user selects a "children's song" and starts singing, but the pitch is off, the server immediately detects the pitch discrepancy and calculates a correction value using a generative AI model. This correction value is then used to modify the audio, and the speaker outputs a sound that sounds like it's in the correct pitch.

[0076] An example of a prompt to a generative AI model would be, "Analyze the pitch and tempo of this audio data and calculate the necessary correction values." This prompt allows the AI ​​to properly analyze the audio data and apply the optimal corrections.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone. The input at this time consists of the selected song information and the user's voice. The user's actions generate a physical acoustic signal from their singing voice.

[0080] Step 2:

[0081] The terminal converts the acoustic signal acquired from the microphone into a digital signal. The input is the user's natural voice (analog signal), and the output is digital audio data. The terminal uses an A / D conversion function to digitize the audio and format it for subsequent network transmission.

[0082] Step 3:

[0083] The terminal transmits digital audio data to the server over the network. The input is digital audio data, and the output is the audio data accurately transferred to the server. The terminal uses network protocols to deliver the data to the server quickly and reliably.

[0084] Step 4:

[0085] The server analyzes the received digital audio signal as pitch and tempo information. The input is digital audio data, and the output is pitch and tempo features. The server executes a signal processing algorithm to analyze the temporal and frequency characteristics of the audio.

[0086] Step 5:

[0087] The server inputs prompt text into the generating AI model, which then calculates correction values ​​to adjust pitch and tempo. The input is the audio features, and the output is the correction values. The server determines the optimal correction parameters through the calculation process performed by the AI ​​model.

[0088] Step 6:

[0089] The server uses the calculated correction values ​​to perform pitch shifting and formant adjustments to correct the audio. The input consists of the correction values ​​and the original audio data, while the output is the corrected audio data. The server utilizes advanced audio signal processing technology to adjust the pitch and tempo while maintaining audio quality.

[0090] Step 7:

[0091] The server sends the corrected audio data to the terminal, which then outputs it through its speaker. The input is the corrected audio data, and the output is clear audio delivered to the audience through the speaker. This allows the user to hear ideal singing audio in real time.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] To enhance the home music entertainment experience, the challenge lies in correcting pitch and tempo discrepancies when users sing along, thereby achieving a professional singing experience. In particular, there is a need to provide users with an easy way to enjoy high-quality music experiences by offering real-time corrected audio output using smartphones and augmented reality devices.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring audio information and converting it into an information signal, means for extracting pitch and speed and calculating correction values, and means for outputting the corrected audio on an augmented reality device or a portable information terminal. This makes it possible for users to easily enjoy a professional-grade karaoke experience at home.

[0097] "Audio information" refers to all data related to sound, including digital information such as human voices and music.

[0098] An "information signal" is a signal that represents audio information in digital format and is used for data analysis and transmission.

[0099] "Pitch" is an indicator of the frequency of sound, and is used to evaluate the pitch of a person's singing voice or a song.

[0100] "Speed" is an indicator that shows the tempo or speed of audio playback, and is used to manage the rhythm of a song.

[0101] A "correction value" is a value calculated to correct deviations in pitch and tempo, and is used to bring the sound closer to the desired state.

[0102] An "augmented reality device" is a device that overlays digital information onto the real world, providing new experiences for sight and hearing.

[0103] "Portable information terminals" refer to all electronic devices that are easy to carry and capable of collecting, processing, and communicating information.

[0104] The system for carrying out the present invention mainly performs voice information acquisition, signal conversion, data analysis, correction processing, and voice output. A specific embodiment thereof is shown below.

[0105] First, the user acquires their own voice using an augmented reality device or a mobile device. Specifically, they collect singing voice using the device's voice input function. The acquired voice information is converted into an information signal in real time and then transmitted to a server via the network.

[0106] The server analyzes the received information signal and extracts pitch and speed. During this process, it utilizes a generative AI model and calculates correction values ​​using a correction model based on past singing data. The software used for this, such as TENSORFLOW®, enables efficient data analysis and model inference.

[0107] The server then corrects the audio based on the calculated correction values. This correction process adjusts the pitch and corrects the tempo to a more natural level. The corrected audio is transmitted to an augmented reality device or mobile device and output to the user in real time. This allows the user to enjoy a professional singing experience from the comfort of their home.

[0108] As a concrete example, when a user sings the latest pop songs via their smartphone in their living room during the daytime, they can experience an augmented reality karaoke session where real-world sounds are blended with enhanced vocals. They can sing along with friends and family in real time, and the enhanced vocals create the desired harmony.

[0109] As an example of a prompt statement,

[0110] "We want to develop an app that corrects pitch and tempo in real time, allowing users to enjoy professional-sounding singing at home."

[0111] "Please suggest ways to improve the karaoke experience using smartphones."

[0112] These are some examples.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device acquires the user's singing voice from the microphone. The input is an analog audio signal, and the output is digital audio information. The data processing performed by the device is the conversion of the analog audio signal into a digital signal. This makes the audio information ready for efficient transmission and analysis.

[0116] Step 2:

[0117] The server receives digital audio information transmitted from the terminal and analyzes its pitch and tempo. The input is digital audio information, and the output is pitch and tempo feature quantities. The data calculation performed by the server involves analyzing the pitch and tempo from the audio signal using the Fourier transform. This analysis allows the server to grasp the structural characteristics of the audio.

[0118] Step 3:

[0119] The server calculates a correction value using the analyzed pitch and tempo. The input is the pitch and tempo features, and the output is the correction value. The server uses a generative AI model to infer an appropriate correction value from a statistical model based on past singing data. This correction value allows for more accurate adjustment of the voice.

[0120] Step 4:

[0121] The server corrects the audio signal using calculated correction values. The input consists of digital audio information and correction values, while the output is the corrected audio information. The data processing performed by the server involves correcting pitch and speed using pitch shifting and time stretching techniques. This process maintains audio quality while performing the correction.

[0122] Step 5:

[0123] The device receives corrected audio information and outputs it to the user in real time. The input is corrected audio information, and the output is audio through the speaker. The device plays this corrected audio, creating a state where the user hears the adjusted singing voice. This allows the user to enjoy a seamless karaoke experience.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] The karaoke support system of the present invention combines voice recognition technology, voice correction technology, and an emotion engine that recognizes the user's emotions to further improve the user's singing experience.

[0126] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice through the microphone and converts the analog audio signal into a digital signal. This audio data is then transmitted to the server via the network.

[0127] The server analyzes the received digital signal and uses an emotion engine to extract pitch, tempo, and emotion. This emotion engine recognizes emotions such as joy, anger, sadness, and happiness from the user's voice.

[0128] Next, based on the emotional information analyzed by the emotion engine, processing is performed to dynamically adjust the pitch and tempo correction values. For example, if a positive emotion is recognized, effects such as slightly speeding up the tempo of the entire song or adding a subtle echo are applied.

[0129] The server uses an AI model trained on past singing data to calculate the necessary pitch correction values. Based on these correction values, pitch correction is performed using pitch shifting technology, and the voice is corrected while maintaining vocal quality through formant adjustment.

[0130] Finally, the corrected audio data is sent back to the device, which then outputs the audio through its speaker. As a result, the user and surrounding audience can experience naturally corrected audio that matches the user's emotions.

[0131] As a concrete example, suppose a user selects a "pop song" and begins singing with a very cheerful mood. The emotion engine recognizes the user's joy, and the server adds appropriate effects along with fine-tuning the tempo. The final output is tailored to the user's emotional state, making the karaoke experience even more enjoyable.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user selects their favorite song on the karaoke machine and begins singing into the microphone.

[0135] Step 2:

[0136] The device acquires the user's singing voice through the microphone and converts this analog audio signal into a digital signal.

[0137] Step 3:

[0138] The terminal transmits the converted digital audio data to the server in real time.

[0139] Step 4:

[0140] The server analyzes the received audio data and extracts pitch and tempo using the Short-Time Fourier Transform (STFT). It also uses an emotion engine to recognize the user's emotions based on the audio features.

[0141] Step 5:

[0142] Based on the analysis results from the emotion engine, the server calculates appropriate correction values ​​to adjust the pitch and tempo according to the user's emotions. For example, if joy is detected, it calculates a correction value to slightly increase the tempo.

[0143] Step 6:

[0144] Based on the calculated correction values, the server uses pitch shifting technology to correct the pitch and adjusts the formant to maintain sound quality while adjusting the voice to match the emotion.

[0145] Step 7:

[0146] The server adds emotion-based effects to the corrected audio data and sends the digital audio data to the terminal.

[0147] Step 8:

[0148] The device outputs corrected and enhanced audio through its speaker in real time, allowing the user and those around them to enjoy the sound.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] Conventional audio processing systems have struggled to adjust sound output while taking user emotions into account, resulting in an inability to achieve natural sound expression. Furthermore, the lack of technology to reflect user emotional information in real time made it difficult for users to have a satisfying singing experience.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for performing calculations to extract emotional information in addition to pitch and speed, means for calculating correction values ​​to adjust performance attributes based on the emotional information, and means for modifying the sound output by applying the correction values. This enables natural and real-time adjustment of the sound output in accordance with the user's emotions.

[0154] An "acoustic signal" is the waveform of an analog or digital sound before it is converted or processed.

[0155] A "digital signal" is a digital format obtained by converting a continuous analog signal into a digital format, which is a format used for processing by digital devices such as computers.

[0156] "Pitch" is an attribute based on the frequency of sound, and it is a characteristic that indicates the height of a sound.

[0157] "Speed" is an attribute that indicates the playback speed or tempo of a song.

[0158] "Emotional information" refers to emotional characteristics extracted from speech or acoustic signals, including emotional states such as joy, anger, sadness, and happiness.

[0159] "Performance attributes" refer to various elements used to adjust the texture and characteristics of the sound output, including tempo and echo.

[0160] A "correction value" is a value calculated to modify the sound output and is an indicator used to adjust pitch, tempo, and performance attributes.

[0161] "To alter" means to change or modify something from its original state.

[0162] "Reproduction" refers to outputting processed acoustic signals as physical sound.

[0163] This invention is a karaoke system that enhances the user's acoustic experience by processing acoustic signals in real time and extracting user emotional information from them, thereby providing natural and appropriate acoustic output.

[0164] First, the user selects a song using the device and begins singing into the microphone. The device then acquires the user's audio signal and converts the analog signal into a digital signal using a dedicated converter. The device then sends the converted digital signal to the server.

[0165] The server analyzes the received digital signal, extracting pitch and velocity, while simultaneously using an emotion engine to analyze emotional information in detail. Based on this emotional information, the server utilizes a generative AI model to calculate appropriate correction values ​​and dynamically modify performance attributes to adjust the acoustic output.

[0166] Ultimately, the server immediately sends the corrected audio signal back to the terminal, which then plays it back in real time through the speaker. This allows users and listeners to experience a natural, emotionally synchronized sound experience.

[0167] For example, if a user starts singing a pop song with high energy, the emotion engine senses the user's excitement. Based on this information, the server adjusts the tempo slightly, adds a light echo, and makes other adjustments. The resulting sound is tailored to the user's emotions, making karaoke more enjoyable.

[0168] An example of a prompt message would be, "Explain how to adjust the sound when the user has selected a pop song and is singing it with positive emotions."

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user selects a song on the karaoke machine and begins singing. The terminal acquires the user's voice through the microphone and converts the analog audio signal into a digital signal. The input is an analog audio signal, and the output is the converted audio data. This conversion is performed using a digital audio converter (DAC).

[0172] Step 2:

[0173] The terminal transmits audio data to the server over the network. The input is digitized audio data, and the output is the data sent to the server. The data is packaged in an appropriate format and compression format.

[0174] Step 3:

[0175] The server analyzes the digital audio data it receives. The input is the digital audio data sent to the server, and the output is the analyzed pitch, velocity, and emotion information. The server uses speech recognition technology to extract pitch and velocity, and an emotion engine to analyze the emotion information.

[0176] Step 4:

[0177] The server uses a generative AI model to calculate correction values ​​based on the extracted emotional information. The inputs are pitch, velocity, and emotional information, and the output is the correction value necessary for adjusting the acoustic attributes. The AI ​​model learns from historical data and dynamically proposes appropriate correction measures.

[0178] Step 5:

[0179] The server prepares to modify the audio output by applying the calculated correction values. The input is the correction value, and the output is the corrected audio data. In this step, pitch shifting and formant adjustments are used to modify the sound while maintaining sound quality.

[0180] Step 6:

[0181] The server transmits the corrected audio data to the terminal. The input is the corrected audio data, and the output is the data transmitted to the terminal. Since the data is processed in real time, transmission is fast and efficient.

[0182] Step 7:

[0183] The device outputs corrected sound data received from the server through its speakers. The input is the corrected sound data received from the server, and the output is the sound that actually reaches the ears. In this step, the user and listener can enjoy natural sound that has been adjusted based on emotional information.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] Conventional karaoke systems correct the pitch and tempo of the voice, but they could not take into account the user's emotional state. As a result, it was difficult for users to pursue a singing experience that fully reflected their emotions. The present invention aims to correct the voice according to the user's emotional state and provide a more moving and natural singing experience.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for acquiring audio data, means for converting the acquired audio data into a digital signal, means for analyzing the digital signal to extract pitch, tempo, and emotional state, means for calculating correction values ​​based on the emotional state, means for correcting the audio using the correction values, and means for outputting the corrected audio. This enables dynamic correction of the audio in accordance with the user's emotional state.

[0189] "Audio data" refers to data that represents an acoustic signal in digital format.

[0190] "Converting to a digital signal" refers to the process of converting an analog audio signal into a digital format.

[0191] "Pitch" refers to the element that indicates the high or low pitch of a sound.

[0192] "Tempo" refers to the speed of music or speech.

[0193] "Emotional state" refers to the emotional state extracted from the user's voice.

[0194] "Correction value" refers to a numerical setting used to adjust pitch, tempo, or effects.

[0195] "Correcting audio" is the process of adjusting the quality of audio by applying correction values.

[0196] "Output method" refers to a function for playing back the corrected audio using sound equipment or speakers.

[0197] "Past singing data" refers to the recorded data of musical performances accumulated to date.

[0198] A "learning model" is an algorithm that learns patterns from past data based on machine learning.

[0199] The system for implementing the present invention consists of three main elements: a terminal, a cloud server, and an audio device.

[0200] The terminal is envisioned to be a mobile information device such as a smartphone or tablet, which has the function of acquiring the user's voice and converting it into a digital signal. Voice input uses the microphone built into the terminal, the acoustic signal is converted into digital data, and it is transmitted to the server via the network.

[0201] The server analyzes the received digital audio data using audio analysis software. This software includes audio processing libraries such as Librosa, which are used to extract pitch, tempo, and emotional state. For emotion recognition, natural language processing APIs such as those provided by Google Cloud are used to recognize emotions from the audio. Based on the emotional state, the server uses a learning model to calculate correction values. This learning model is generated by analyzing past singing data, enabling voice adjustments that respond to the user's emotions. The SoX library is used to correct pitch and tempo, correcting the audio in real time.

[0202] The corrected audio data is retransmitted to the device and output through the device's speaker. This allows the user to experience an adjusted voice that matches their emotions in real time. For example, when a user sings a pop song with a "happy" feeling, the server recognizes the emotion, slightly speeds up the tempo, and adds appropriate effects to the voice. As a result, the user can enjoy karaoke even more.

[0203] Example prompt to input into the generative AI model: "Identify the user's emotions from this audio data and suggest appropriate sound effects."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user launches the karaoke app on their device and selects a song. The information for the selected song is loaded onto the device, and it is ready to sing. The user's voice is captured in real time via the microphone. The input is the user's voice, and the output is an analog audio signal.

[0207] Step 2:

[0208] The terminal converts the analog audio signal acquired by the microphone into digital data using a digital signal processing module. This digital data is then prepared for transmission to the server. The input is an analog audio signal, and the output is digital audio data.

[0209] Step 3:

[0210] The server receives digital audio data transmitted from the terminal. Using the Librosa library, it extracts pitch, tempo, and emotional state using an emotion recognition engine. The input is digital audio data, and the output is information on pitch, tempo, and emotional state.

[0211] Step 4:

[0212] The server calculates pitch and tempo correction values ​​based on the emotional state, using a learning model that utilizes past singing data. Furthermore, it uses the SoX library to perform real-time pitch shifting of the audio based on these correction values. The input is information on pitch, tempo, and emotional state, and the output is the corrected audio data.

[0213] Step 5:

[0214] The server sends the corrected audio data to the terminal. The terminal plays the received audio data through its speaker. This allows the user to hear emotion-adjusted audio in real time. The input is the corrected audio data, and the output is the audio output.

[0215] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0231] The karaoke support system of the present invention improves the user's singing experience using speech recognition technology and speech correction technology.

[0232] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice from the microphone in real time and converts the audio into a digital signal. This audio data is then transmitted to a server via the network.

[0233] The server analyzes the received digital signal and extracts pitch and tempo from the audio. Next, it uses an AI model trained on past singing data to calculate the necessary correction values ​​from the extracted audio features. The server uses these correction values ​​to correct pitch deviations and tempo mismatches in real time. In particular, for pitch correction, it utilizes pitch shifting technology and formant adjustment to correct the voice quality without compromising it.

[0234] The corrected audio data is sent back to the device, which then outputs the audio through its speaker. This process allows the user and surrounding audience to enjoy a more harmonious and pleasant singing voice.

[0235] For example, when a user selects a children's song and begins to sing, if the pitch is off by a semitone, the server detects the discrepancy and calculates a correction value. By correcting the audio based on this value, the final sound output from the speaker will sound as if it were sung in the correct pitch. In this way, the system makes it possible to overcome tone-deafness and enjoy karaoke more.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone.

[0239] Step 2:

[0240] The device acquires the user's singing voice through the microphone and converts that analog signal into a digital signal.

[0241] Step 3:

[0242] The terminal transmits digital audio data acquired in real time to the server via the network.

[0243] Step 4:

[0244] The server applies a Short-Time Fourier Transform (STFT) to the received audio data to analyze and extract pitch and tempo.

[0245] Step 5:

[0246] The server inputs the analysis results into the AI ​​model and calculates correction values ​​using the model, which has been trained based on past singing data.

[0247] Step 6:

[0248] The server uses pitch shifting technology to correct the pitch of the user's voice and maintains voice quality by adjusting formants as needed.

[0249] Step 7:

[0250] The server adjusts for tempo discrepancies, generates corrected audio data at the appropriate timing, and sends it to the terminal.

[0251] Step 8:

[0252] The device outputs the corrected audio data in real time through its speaker, allowing the user and those around them to hear harmonious audio.

[0253] (Example 1)

[0254] Next, we will describe Example 1. 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."

[0255] In audio recording and broadcasting, inaccurate pitch and tempo can create an unpleasant impression on listeners. This is especially true in situations requiring real-time audio output, such as karaoke, where correcting audio errors immediately is difficult. Therefore, there is a need to instantly correct pitch and tempo discrepancies to provide more natural-sounding audio.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for converting audio data into a digital signal, means for calculating correction values ​​for correcting pitch and tempo using a generation AI model, and means for correcting the audio by performing pitch shifting and formant adjustment. This makes it possible to provide more harmonious audio by correcting pitch and tempo errors in real time.

[0258] "Audio data" refers to information that is composed of sound in digital format and is used to represent acoustic signals.

[0259] A "digital signal" is a data format obtained by sampling and quantizing analog information, and is primarily suited for processing by electronic computers.

[0260] "Interval" refers to a characteristic that indicates the height of a sound, and it forms the basis of melody and chords in music.

[0261] "Tempo" refers to the speed of music and is a factor that determines the pace at which a song progresses.

[0262] A "correction value" is a numerical value calculated to correct errors or inconsistencies, and is used in audio processing to adjust pitch and tempo.

[0263] "Pitch shifting technology" is a technique that changes the pitch of an audio signal and is used to generate sounds of different pitches while maintaining sound quality.

[0264] "Formant adjustment" is a technique for changing the pitch of a voice without altering its quality, and is an acoustic processing method that maintains a natural-sounding voice.

[0265] A "generative AI model" is an artificial intelligence algorithm that uses learning based on past data to perform predictions and classifications.

[0266] The karaoke support system of the present invention uses speech recognition technology and speech correction technology to improve the user's singing experience. Specifically, it enables high-level real-time processing of the voice as the user sings on the karaoke machine.

[0267] The user selects a song on the karaoke machine and begins singing. The terminal captures the user's voice input from the microphone and converts the analog signal into a digital signal. This conversion needs to be highly accurate, and dedicated audio processing software is typically used. The terminal then transmits this digital audio data to a server via the network.

[0268] The server analyzes the received digital audio signal and extracts pitch and tempo from the audio. The extracted data is input into a generative AI model, and the AI, which has learned from past singing data, calculates correction values. Based on these correction values, the server corrects pitch deviations and tempo mismatches using pitch shifting technology and formant adjustment. Through this process, the corrected audio data is provided in real time as a more harmonious and natural-sounding voice.

[0269] The corrected audio data is sent back to the device, which then uses that data to output the corrected audio through its speakers. This technology allows users to check their own singing voice in real time while enjoying a comfortable music experience with those around them.

[0270] For example, if a user selects a "children's song" and starts singing, but the pitch is off, the server immediately detects the pitch discrepancy and calculates a correction value using a generative AI model. This correction value is then used to modify the audio, and the speaker outputs a sound that sounds like it's in the correct pitch.

[0271] An example of a prompt to a generative AI model would be, "Analyze the pitch and tempo of this audio data and calculate the necessary correction values." This prompt allows the AI ​​to properly analyze the audio data and apply the optimal corrections.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone. The input at this time consists of the selected song information and the user's voice. The user's actions generate a physical acoustic signal from their singing voice.

[0275] Step 2:

[0276] The terminal converts the acoustic signal acquired from the microphone into a digital signal. The input is the user's natural voice (analog signal), and the output is digital audio data. The terminal uses an A / D conversion function to digitize the audio and format it for subsequent network transmission.

[0277] Step 3:

[0278] The terminal transmits digital audio data to the server over the network. The input is digital audio data, and the output is the audio data accurately transferred to the server. The terminal uses network protocols to deliver the data to the server quickly and reliably.

[0279] Step 4:

[0280] The server analyzes the received digital audio signal as pitch and tempo information. The input is digital audio data, and the output is pitch and tempo feature quantities. The server executes a signal processing algorithm to analyze the temporal and frequency characteristics of the audio.

[0281] Step 5:

[0282] The server inputs a prompt sentence into the generative AI model to calculate correction values for correcting pitch and tempo. The input is the feature quantity of the audio, and the output is the correction value. The server obtains optimal correction parameters through the calculation process by the AI model.

[0283] Step 6:

[0284] The server performs pitch shifting technology and formant adjustment using the calculated correction values to correct the audio. The input is the correction value and the original audio data, and the output is the corrected audio data. The server makes full use of audio signal processing technology to adjust pitch and tempo while maintaining the quality of the audio.

[0285] Step 7:

[0286] The server transmits the corrected audio data to the terminal, and the terminal outputs it from the speaker. The input is the corrected audio data, and the output is clear audio delivered to the audience through the speaker. Thereby, the user can listen to the ideal singing voice in real time.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] To enhance the home music entertainment experience, the challenge lies in correcting pitch and tempo discrepancies when users sing along, thereby achieving a professional singing experience. In particular, there is a need to provide users with an easy way to enjoy high-quality music experiences by offering real-time corrected audio output using smartphones and augmented reality devices.

[0290] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for acquiring audio information and converting it into an information signal, means for extracting pitch and speed and calculating correction values, and means for outputting the corrected audio on an augmented reality device or a portable information terminal. This makes it possible for users to easily enjoy a professional-grade karaoke experience at home.

[0292] "Audio information" refers to all data related to sound, including digital information such as human voices and music.

[0293] An "information signal" is a signal that represents audio information in digital format and is used for data analysis and transmission.

[0294] "Pitch" is an indicator of the frequency of sound, and is used to evaluate the pitch of a person's singing voice or a song.

[0295] "Speed" is an indicator that shows the tempo or speed of audio playback, and is used to manage the rhythm of a song.

[0296] A "correction value" is a value calculated to correct deviations in pitch and tempo, and is used to bring the sound closer to the desired state.

[0297] An "augmented reality device" is a device that overlays digital information onto the real world, providing new experiences for sight and hearing.

[0298] "Portable information terminals" refer to all electronic devices that are easy to carry and capable of collecting, processing, and communicating information.

[0299] The system for carrying out the present invention mainly performs voice information acquisition, signal conversion, data analysis, correction processing, and voice output. A specific embodiment thereof is shown below.

[0300] First, the user acquires their own voice using an augmented reality device or a mobile device. Specifically, they collect singing voice using the device's voice input function. The acquired voice information is converted into an information signal in real time and then transmitted to a server via the network.

[0301] The server analyzes the received information signal and extracts pitch and speed. During this process, it utilizes a generative AI model and calculates correction values ​​using a correction model based on past singing data. The software used for this is, for example, TensorFlow, which allows for efficient data analysis and model inference.

[0302] The server then corrects the audio based on the calculated correction values. This correction process adjusts the pitch and corrects the tempo to a more natural level. The corrected audio is transmitted to an augmented reality device or mobile device and output to the user in real time. This allows the user to enjoy a professional singing experience from the comfort of their home.

[0303] As a concrete example, when a user sings the latest pop songs via their smartphone in their living room during the daytime, they can experience an augmented reality karaoke session where real-world sounds are blended with enhanced vocals. They can sing along with friends and family in real time, and the enhanced vocals create the desired harmony.

[0304] As an example of a prompt statement,

[0305] "I want to develop an app that can correct pitch and tempo in real time and allow users to enjoy professional-quality singing at home."

[0306] "Please propose a method to improve the karaoke experience using a smartphone."

[0307] can be cited.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The terminal acquires the user's singing voice from the microphone. The input is an analog audio signal, and the output is digital audio information. The data processing performed by the terminal is to convert the audio analog signal into a digital signal. This enables efficient transmission and analysis of the audio information.

[0311] Step 2:

[0312] The server receives the digital audio information transmitted from the terminal and analyzes the pitch and speed. The input is digital audio information, and the output is the feature quantities of pitch and speed. The data calculation performed by the server is to analyze the pitch and tempo from the audio signal using the Fourier transform. By this analysis, the structural features of the audio are grasped.

[0313] Step 3:

[0314] The server calculates a correction value using the analyzed pitch and speed. The input is the feature quantities of pitch and speed, and the output is the correction value. The server uses a generated AI model to infer an appropriate correction value from a statistical model based on past singing data. With this correction value, the audio can be adjusted more accurately.

[0315] Step 4:

[0316] The server corrects the audio signal using calculated correction values. The input consists of digital audio information and correction values, while the output is the corrected audio information. The data processing performed by the server involves correcting pitch and speed using pitch shifting and time stretching techniques. This process maintains audio quality while performing the correction.

[0317] Step 5:

[0318] The device receives corrected audio information and outputs it to the user in real time. The input is corrected audio information, and the output is audio through the speaker. The device plays this corrected audio, creating a state where the user hears the adjusted singing voice. This allows the user to enjoy a seamless karaoke experience.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] The karaoke support system of the present invention combines voice recognition technology, voice correction technology, and an emotion engine that recognizes the user's emotions to further improve the user's singing experience.

[0321] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice through the microphone and converts the analog audio signal into a digital signal. This audio data is then transmitted to the server via the network.

[0322] The server analyzes the received digital signal and uses an emotion engine to extract pitch, tempo, and emotion. This emotion engine recognizes emotions such as joy, anger, sadness, and happiness from the user's voice.

[0323] Next, based on the emotional information analyzed by the emotion engine, processing is performed to dynamically adjust the pitch and tempo correction values. For example, if a positive emotion is recognized, effects such as slightly speeding up the tempo of the entire song or adding a subtle echo are applied.

[0324] The server uses an AI model trained on past singing data to calculate the necessary pitch correction values. Based on these correction values, pitch correction is performed using pitch shifting technology, and the voice is corrected while maintaining vocal quality through formant adjustment.

[0325] Finally, the corrected audio data is sent back to the device, which then outputs the audio through its speaker. As a result, the user and surrounding audience can experience naturally corrected audio that matches the user's emotions.

[0326] As a concrete example, suppose a user selects a "pop song" and begins singing with a very cheerful mood. The emotion engine recognizes the user's joy, and the server adds appropriate effects along with fine-tuning the tempo. The final output is tailored to the user's emotional state, making the karaoke experience even more enjoyable.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user selects their favorite song on the karaoke machine and begins singing into the microphone.

[0330] Step 2:

[0331] The device acquires the user's singing voice through the microphone and converts this analog audio signal into a digital signal.

[0332] Step 3:

[0333] The terminal transmits the converted digital audio data to the server in real time.

[0334] Step 4:

[0335] The server analyzes the received audio data and extracts pitch and tempo using the Short-Time Fourier Transform (STFT). It also uses an emotion engine to recognize the user's emotions based on the audio features.

[0336] Step 5:

[0337] Based on the analysis results from the emotion engine, the server calculates appropriate correction values ​​to adjust the pitch and tempo according to the user's emotions. For example, if joy is detected, it calculates a correction value to slightly increase the tempo.

[0338] Step 6:

[0339] Based on the calculated correction values, the server uses pitch shifting technology to correct the pitch and adjusts the formant to maintain sound quality while adjusting the voice to match the emotion.

[0340] Step 7:

[0341] The server adds emotion-based effects to the corrected audio data and sends the digital audio data to the terminal.

[0342] Step 8:

[0343] The device outputs corrected and enhanced audio through its speaker in real time, allowing the user and those around them to enjoy the sound.

[0344] (Example 2)

[0345] Next, we will describe Example 2. 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".

[0346] Conventional audio processing systems have struggled to adjust sound output while taking user emotions into account, resulting in an inability to achieve natural sound expression. Furthermore, the lack of technology to reflect user emotional information in real time made it difficult for users to have a satisfying singing experience.

[0347] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0348] In this invention, the server includes means for performing calculations to extract emotional information in addition to pitch and speed, means for calculating correction values ​​to adjust performance attributes based on the emotional information, and means for modifying the sound output by applying the correction values. This enables natural and real-time adjustment of the sound output in accordance with the user's emotions.

[0349] An "acoustic signal" is the waveform of an analog or digital sound before it is converted or processed.

[0350] A "digital signal" is a digital format obtained by converting a continuous analog signal into a digital format, which is a format used for processing by digital devices such as computers.

[0351] "Pitch" is an attribute based on the frequency of sound, and it is a characteristic that indicates the height of a sound.

[0352] "Speed" is an attribute that indicates the playback speed or tempo of a song.

[0353] "Emotional information" refers to emotional characteristics extracted from speech or acoustic signals, including emotional states such as joy, anger, sadness, and happiness.

[0354] "Performance attributes" refer to various elements used to adjust the texture and characteristics of the sound output, including tempo and echo.

[0355] A "correction value" is a value calculated to modify the sound output and is an indicator used to adjust pitch, tempo, and performance attributes.

[0356] "To alter" means to change or modify something from its original state.

[0357] "Reproduction" refers to outputting processed acoustic signals as physical sound.

[0358] This invention is a karaoke system that enhances the user's acoustic experience by processing acoustic signals in real time and extracting user emotional information from them, thereby providing natural and appropriate acoustic output.

[0359] First, the user selects a song using the device and begins singing into the microphone. The device then acquires the user's audio signal and converts the analog signal into a digital signal using a dedicated converter. The device then sends the converted digital signal to the server.

[0360] The server analyzes the received digital signal, extracting pitch and velocity, while simultaneously using an emotion engine to analyze emotional information in detail. Based on this emotional information, the server utilizes a generative AI model to calculate appropriate correction values ​​and dynamically modify performance attributes to adjust the acoustic output.

[0361] Ultimately, the server immediately sends the corrected audio signal back to the terminal, which then plays it back in real time through the speaker. This allows users and listeners to experience a natural, emotionally synchronized sound experience.

[0362] For example, if a user starts singing a pop song with high energy, the emotion engine senses the user's excitement. Based on this information, the server adjusts the tempo slightly, adds a light echo, and makes other adjustments. The resulting sound is tailored to the user's emotions, making karaoke more enjoyable.

[0363] An example of a prompt message would be, "Explain how to adjust the sound when the user has selected a pop song and is singing it with positive emotions."

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user selects a song on the karaoke machine and begins singing. The terminal acquires the user's voice through the microphone and converts the analog audio signal into a digital signal. The input is an analog audio signal, and the output is the converted audio data. This conversion is performed using a digital audio converter (DAC).

[0367] Step 2:

[0368] The terminal transmits audio data to the server over the network. The input is digitized audio data, and the output is the data sent to the server. The data is packaged in an appropriate format and compression format.

[0369] Step 3:

[0370] The server analyzes the digital audio data it receives. The input is the digital audio data sent to the server, and the output is the analyzed pitch, velocity, and emotion information. The server uses speech recognition technology to extract pitch and velocity, and an emotion engine to analyze the emotion information.

[0371] Step 4:

[0372] The server uses a generative AI model to calculate correction values ​​based on the extracted emotional information. The inputs are pitch, velocity, and emotional information, and the output is the correction value necessary for adjusting the acoustic attributes. The AI ​​model learns from historical data and dynamically proposes appropriate correction measures.

[0373] Step 5:

[0374] The server prepares to modify the audio output by applying the calculated correction values. The input is the correction value, and the output is the corrected audio data. In this step, pitch shifting and formant adjustments are used to modify the sound while maintaining sound quality.

[0375] Step 6:

[0376] The server transmits the corrected audio data to the terminal. The input is the corrected audio data, and the output is the data transmitted to the terminal. Since the data is processed in real time, transmission is fast and efficient.

[0377] Step 7:

[0378] The device outputs corrected sound data received from the server through its speakers. The input is the corrected sound data received from the server, and the output is the sound that actually reaches the ears. In this step, the user and listener can enjoy natural sound that has been adjusted based on emotional information.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] Conventional karaoke systems correct the pitch and tempo of the voice, but they could not take into account the user's emotional state. As a result, it was difficult for users to pursue a singing experience that fully reflected their emotions. The present invention aims to correct the voice according to the user's emotional state and provide a more moving and natural singing experience.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for acquiring audio data, means for converting the acquired audio data into a digital signal, means for analyzing the digital signal to extract pitch, tempo, and emotional state, means for calculating correction values ​​based on the emotional state, means for correcting the audio using the correction values, and means for outputting the corrected audio. This enables dynamic correction of the audio in accordance with the user's emotional state.

[0384] "Audio data" refers to data that represents an acoustic signal in digital format.

[0385] "Converting to a digital signal" refers to the process of converting an analog audio signal into a digital format.

[0386] "Pitch" refers to the element that indicates the high or low pitch of a sound.

[0387] "Tempo" refers to the speed of music or speech.

[0388] "Emotional state" refers to the emotional state extracted from the user's voice.

[0389] "Correction value" refers to a numerical setting used to adjust pitch, tempo, or effects.

[0390] "Correcting audio" is the process of adjusting the quality of audio by applying correction values.

[0391] "Output method" refers to a function for playing back the corrected audio using sound equipment or speakers.

[0392] "Past singing data" refers to the recorded data of musical performances accumulated to date.

[0393] A "learning model" is an algorithm that learns patterns from past data based on machine learning.

[0394] The system for implementing the present invention consists of three main elements: a terminal, a cloud server, and an audio device.

[0395] The terminal is envisioned to be a mobile information device such as a smartphone or tablet, which has the function of acquiring the user's voice and converting it into a digital signal. Voice input uses the microphone built into the terminal, the acoustic signal is converted into digital data, and it is transmitted to the server via the network.

[0396] The server analyzes the received digital audio data using audio analysis software. This software includes audio processing libraries such as Librosa, which are used to extract pitch, tempo, and emotional state. For emotion recognition, natural language processing APIs such as those provided by Google Cloud are used to recognize emotions from the audio. Based on the emotional state, the server uses a learning model to calculate correction values. This learning model is generated by analyzing past singing data, enabling voice adjustments that respond to the user's emotions. The SoX library is used to correct pitch and tempo, correcting the audio in real time.

[0397] The corrected audio data is retransmitted to the device and output through the device's speaker. This allows the user to experience an adjusted voice that matches their emotions in real time. For example, when a user sings a pop song with a "happy" feeling, the server recognizes the emotion, slightly speeds up the tempo, and adds appropriate effects to the voice. As a result, the user can enjoy karaoke even more.

[0398] Example prompt to input into the generative AI model: "Identify the user's emotions from this audio data and suggest appropriate sound effects."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user launches the karaoke app on their device and selects a song. The information for the selected song is loaded onto the device, and it is ready to sing. The user's voice is captured in real time via the microphone. The input is the user's voice, and the output is an analog audio signal.

[0402] Step 2:

[0403] The terminal converts the analog audio signal acquired by the microphone into digital data using a digital signal processing module. This digital data is then prepared for transmission to the server. The input is an analog audio signal, and the output is digital audio data.

[0404] Step 3:

[0405] The server receives digital audio data transmitted from the terminal. Using the Librosa library, it extracts pitch, tempo, and emotional state using an emotion recognition engine. The input is digital audio data, and the output is information on pitch, tempo, and emotional state.

[0406] Step 4:

[0407] The server calculates pitch and tempo correction values ​​based on the emotional state, using a learning model that utilizes past singing data. Furthermore, it uses the SoX library to perform real-time pitch shifting of the audio based on these correction values. The input is information on pitch, tempo, and emotional state, and the output is the corrected audio data.

[0408] Step 5:

[0409] The server sends the corrected audio data to the terminal. The terminal plays the received audio data through its speaker. This allows the user to hear emotion-adjusted audio in real time. The input is the corrected audio data, and the output is the audio output.

[0410] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0416] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0417] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0418] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0420] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0421] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0422] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0423] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0424] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0425] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0426] The karaoke support system of the present invention improves the user's singing experience using speech recognition technology and speech correction technology.

[0427] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice from the microphone in real time and converts the audio into a digital signal. This audio data is then transmitted to a server via the network.

[0428] The server analyzes the received digital signal and extracts pitch and tempo from the audio. Next, it uses an AI model trained on past singing data to calculate the necessary correction values ​​from the extracted audio features. The server uses these correction values ​​to correct pitch deviations and tempo mismatches in real time. In particular, for pitch correction, it utilizes pitch shifting technology and formant adjustment to correct the voice quality without compromising it.

[0429] The corrected audio data is sent back to the device, which then outputs the audio through its speaker. This process allows the user and surrounding audience to enjoy a more harmonious and pleasant singing voice.

[0430] For example, when a user selects a children's song and begins to sing, if the pitch is off by a semitone, the server detects the discrepancy and calculates a correction value. By correcting the audio based on this value, the final sound output from the speaker will sound as if it were sung in the correct pitch. In this way, the system makes it possible to overcome tone-deafness and enjoy karaoke more.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone.

[0434] Step 2:

[0435] The device acquires the user's singing voice through the microphone and converts that analog signal into a digital signal.

[0436] Step 3:

[0437] The terminal transmits digital audio data acquired in real time to the server via the network.

[0438] Step 4:

[0439] The server applies a Short-Time Fourier Transform (STFT) to the received audio data to analyze and extract pitch and tempo.

[0440] Step 5:

[0441] The server inputs the analysis results into the AI ​​model and calculates correction values ​​using the model, which has been trained based on past singing data.

[0442] Step 6:

[0443] The server uses pitch shifting technology to correct the pitch of the user's voice and maintains voice quality by adjusting formants as needed.

[0444] Step 7:

[0445] The server adjusts for tempo discrepancies, generates corrected audio data at the appropriate timing, and sends it to the terminal.

[0446] Step 8:

[0447] The device outputs the corrected audio data in real time through its speaker, allowing the user and those around them to hear harmonious audio.

[0448] (Example 1)

[0449] Next, we will describe Example 1. 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."

[0450] In audio recording and broadcasting, inaccurate pitch and tempo can create an unpleasant impression on listeners. This is especially true in situations requiring real-time audio output, such as karaoke, where correcting audio errors immediately is difficult. Therefore, there is a need to instantly correct pitch and tempo discrepancies to provide more natural-sounding audio.

[0451] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0452] In this invention, the server includes means for converting audio data into a digital signal, means for calculating correction values ​​for correcting pitch and tempo using a generation AI model, and means for correcting the audio by performing pitch shifting and formant adjustment. This makes it possible to provide more harmonious audio by correcting pitch and tempo errors in real time.

[0453] "Audio data" refers to information that is composed of sound in digital format and is used to represent acoustic signals.

[0454] A "digital signal" is a data format obtained by sampling and quantizing analog information, and is primarily suited for processing by electronic computers.

[0455] "Interval" refers to a characteristic that indicates the height of a sound, and it forms the basis of melody and chords in music.

[0456] "Tempo" refers to the speed of music and is a factor that determines the pace at which a song progresses.

[0457] A "correction value" is a numerical value calculated to correct errors or inconsistencies, and is used in audio processing to adjust pitch and tempo.

[0458] "Pitch shifting technology" is a technique that changes the pitch of an audio signal and is used to generate sounds of different pitches while maintaining sound quality.

[0459] "Formant adjustment" is a technique for changing the pitch of a voice without altering its quality, and is an acoustic processing method that maintains a natural-sounding voice.

[0460] A "generative AI model" is an artificial intelligence algorithm that uses learning based on past data to perform predictions and classifications.

[0461] The karaoke support system of the present invention uses speech recognition technology and speech correction technology to improve the user's singing experience. Specifically, it enables high-level real-time processing of the voice as the user sings on the karaoke machine.

[0462] The user selects a song on the karaoke machine and begins singing. The terminal captures the user's voice input from the microphone and converts the analog signal into a digital signal. This conversion needs to be highly accurate, and dedicated audio processing software is typically used. The terminal then transmits this digital audio data to a server via the network.

[0463] The server analyzes the received digital audio signal and extracts pitch and tempo from the audio. The extracted data is input into a generative AI model, and the AI, which has learned from past singing data, calculates correction values. Based on these correction values, the server corrects pitch deviations and tempo mismatches using pitch shifting technology and formant adjustment. Through this process, the corrected audio data is provided in real time as a more harmonious and natural-sounding voice.

[0464] The corrected audio data is sent back to the device, which then uses that data to output the corrected audio through its speakers. This technology allows users to check their own singing voice in real time while enjoying a comfortable music experience with those around them.

[0465] For example, if a user selects a "children's song" and starts singing, but the pitch is off, the server immediately detects the pitch discrepancy and calculates a correction value using a generative AI model. This correction value is then used to modify the audio, and the speaker outputs a sound that sounds like it's in the correct pitch.

[0466] An example of a prompt to a generative AI model would be, "Analyze the pitch and tempo of this audio data and calculate the necessary correction values." This prompt allows the AI ​​to properly analyze the audio data and apply the optimal corrections.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone. The input at this time consists of the selected song information and the user's voice. The user's actions generate a physical acoustic signal from their singing voice.

[0470] Step 2:

[0471] The terminal converts the acoustic signal acquired from the microphone into a digital signal. The input is the user's natural voice (analog signal), and the output is digital audio data. The terminal uses an A / D conversion function to digitize the audio and format it for subsequent network transmission.

[0472] Step 3:

[0473] The terminal transmits digital audio data to the server over the network. The input is digital audio data, and the output is the audio data accurately transferred to the server. The terminal uses network protocols to deliver the data to the server quickly and reliably.

[0474] Step 4:

[0475] The server analyzes the received digital audio signal as pitch and tempo information. The input is digital audio data, and the output is pitch and tempo features. The server executes a signal processing algorithm to analyze the temporal and frequency characteristics of the audio.

[0476] Step 5:

[0477] The server inputs prompt text into the generating AI model, which then calculates correction values ​​to adjust pitch and tempo. The input is the audio features, and the output is the correction values. The server determines the optimal correction parameters through the calculation process performed by the AI ​​model.

[0478] Step 6:

[0479] The server uses the calculated correction values ​​to perform pitch shifting and formant adjustments to correct the audio. The input consists of the correction values ​​and the original audio data, while the output is the corrected audio data. The server utilizes advanced audio signal processing technology to adjust the pitch and tempo while maintaining audio quality.

[0480] Step 7:

[0481] The server sends the corrected audio data to the terminal, which then outputs it through its speaker. The input is the corrected audio data, and the output is clear audio delivered to the audience through the speaker. This allows the user to hear ideal singing audio in real time.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, 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."

[0484] To enhance the home music entertainment experience, the challenge lies in correcting pitch and tempo discrepancies when users sing along, thereby achieving a professional singing experience. In particular, there is a need to provide users with an easy way to enjoy high-quality music experiences by offering real-time corrected audio output using smartphones and augmented reality devices.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes means for acquiring audio information and converting it into an information signal, means for extracting pitch and speed and calculating correction values, and means for outputting the corrected audio on an augmented reality device or a portable information terminal. This makes it possible for users to easily enjoy a professional-grade karaoke experience at home.

[0487] "Audio information" refers to all data related to sound, including digital information such as human voices and music.

[0488] An "information signal" is a signal that represents audio information in digital format and is used for data analysis and transmission.

[0489] "Pitch" is an indicator of the frequency of sound, and is used to evaluate the pitch of a person's singing voice or a song.

[0490] "Speed" is an indicator that shows the tempo or speed of audio playback, and is used to manage the rhythm of a song.

[0491] A "correction value" is a value calculated to correct deviations in pitch and tempo, and is used to bring the sound closer to the desired state.

[0492] An "augmented reality device" is a device that overlays digital information onto the real world, providing new experiences for sight and hearing.

[0493] "Portable information terminals" refer to all electronic devices that are easy to carry and capable of collecting, processing, and communicating information.

[0494] The system for carrying out the present invention mainly performs voice information acquisition, signal conversion, data analysis, correction processing, and voice output. A specific embodiment thereof is shown below.

[0495] First, the user acquires their own voice using an augmented reality device or a mobile device. Specifically, they collect singing voice using the device's voice input function. The acquired voice information is converted into an information signal in real time and then transmitted to a server via the network.

[0496] The server analyzes the received information signal and extracts pitch and speed. During this process, it utilizes a generative AI model and calculates correction values ​​using a correction model based on past singing data. The software used for this is, for example, TensorFlow, which allows for efficient data analysis and model inference.

[0497] The server then corrects the audio based on the calculated correction values. This correction process adjusts the pitch and corrects the tempo to a more natural level. The corrected audio is transmitted to an augmented reality device or mobile device and output to the user in real time. This allows the user to enjoy a professional singing experience from the comfort of their home.

[0498] As a concrete example, when a user sings the latest pop songs via their smartphone in their living room during the daytime, they can experience an augmented reality karaoke session where real-world sounds are blended with enhanced vocals. They can sing along with friends and family in real time, and the enhanced vocals create the desired harmony.

[0499] As an example of a prompt statement,

[0500] "We want to develop an app that corrects pitch and tempo in real time, allowing users to enjoy professional-sounding singing at home."

[0501] "Please suggest ways to improve the karaoke experience using smartphones."

[0502] These are some examples.

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The device acquires the user's singing voice from the microphone. The input is an analog audio signal, and the output is digital audio information. The data processing performed by the device is the conversion of the analog audio signal into a digital signal. This makes the audio information ready for efficient transmission and analysis.

[0506] Step 2:

[0507] The server receives digital audio information transmitted from the terminal and analyzes its pitch and tempo. The input is digital audio information, and the output is pitch and tempo feature quantities. The data calculation performed by the server involves analyzing the pitch and tempo from the audio signal using the Fourier transform. This analysis allows the server to grasp the structural characteristics of the audio.

[0508] Step 3:

[0509] The server calculates a correction value using the analyzed pitch and tempo. The input is the pitch and tempo features, and the output is the correction value. The server uses a generative AI model to infer an appropriate correction value from a statistical model based on past singing data. This correction value allows for more accurate adjustment of the voice.

[0510] Step 4:

[0511] The server corrects the audio signal using calculated correction values. The input consists of digital audio information and correction values, while the output is the corrected audio information. The data processing performed by the server involves correcting pitch and speed using pitch shifting and time stretching techniques. This process maintains audio quality while performing the correction.

[0512] Step 5:

[0513] The device receives corrected audio information and outputs it to the user in real time. The input is corrected audio information, and the output is audio through the speaker. The device plays this corrected audio, creating a state where the user hears the adjusted singing voice. This allows the user to enjoy a seamless karaoke experience.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] The karaoke support system of the present invention combines voice recognition technology, voice correction technology, and an emotion engine that recognizes the user's emotions to further improve the user's singing experience.

[0516] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice through the microphone and converts the analog audio signal into a digital signal. This audio data is then transmitted to the server via the network.

[0517] The server analyzes the received digital signal and uses an emotion engine to extract pitch, tempo, and emotion. This emotion engine recognizes emotions such as joy, anger, sadness, and happiness from the user's voice.

[0518] Next, based on the emotional information analyzed by the emotion engine, processing is performed to dynamically adjust the pitch and tempo correction values. For example, if a positive emotion is recognized, effects such as slightly speeding up the tempo of the entire song or adding a subtle echo are applied.

[0519] The server uses an AI model trained on past singing data to calculate the necessary pitch correction values. Based on these correction values, pitch correction is performed using pitch shifting technology, and the voice is corrected while maintaining vocal quality through formant adjustment.

[0520] Finally, the corrected audio data is sent back to the device, which then outputs the audio through its speaker. As a result, the user and surrounding audience can experience naturally corrected audio that matches the user's emotions.

[0521] As a concrete example, suppose a user selects a "pop song" and begins singing with a very cheerful mood. The emotion engine recognizes the user's joy, and the server adds appropriate effects along with fine-tuning the tempo. The final output is tailored to the user's emotional state, making the karaoke experience even more enjoyable.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user selects their favorite song on the karaoke machine and begins singing into the microphone.

[0525] Step 2:

[0526] The device acquires the user's singing voice through the microphone and converts this analog audio signal into a digital signal.

[0527] Step 3:

[0528] The terminal transmits the converted digital audio data to the server in real time.

[0529] Step 4:

[0530] The server analyzes the received audio data and extracts pitch and tempo using the Short-Time Fourier Transform (STFT). It also uses an emotion engine to recognize the user's emotions based on the audio features.

[0531] Step 5:

[0532] Based on the analysis results from the emotion engine, the server calculates appropriate correction values ​​to adjust the pitch and tempo according to the user's emotions. For example, if joy is detected, it calculates a correction value to slightly increase the tempo.

[0533] Step 6:

[0534] Based on the calculated correction values, the server uses pitch shifting technology to correct the pitch and adjusts the formant to maintain sound quality while adjusting the voice to match the emotion.

[0535] Step 7:

[0536] The server adds emotion-based effects to the corrected audio data and sends the digital audio data to the terminal.

[0537] Step 8:

[0538] The device outputs corrected and enhanced audio through its speaker in real time, allowing the user and those around them to enjoy the sound.

[0539] (Example 2)

[0540] Next, we will describe Example 2. 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."

[0541] Conventional audio processing systems have struggled to adjust sound output while taking user emotions into account, resulting in an inability to achieve natural sound expression. Furthermore, the lack of technology to reflect user emotional information in real time made it difficult for users to have a satisfying singing experience.

[0542] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0543] In this invention, the server includes means for performing calculations to extract emotional information in addition to pitch and speed, means for calculating correction values ​​to adjust performance attributes based on the emotional information, and means for modifying the sound output by applying the correction values. This enables natural and real-time adjustment of the sound output in accordance with the user's emotions.

[0544] An "acoustic signal" is the waveform of an analog or digital sound before it is converted or processed.

[0545] A "digital signal" is a digital format obtained by converting a continuous analog signal into a digital format, which is a format used for processing by digital devices such as computers.

[0546] "Pitch" is an attribute based on the frequency of sound, and it is a characteristic that indicates the height of a sound.

[0547] "Speed" is an attribute that indicates the playback speed or tempo of a song.

[0548] "Emotional information" refers to emotional characteristics extracted from speech or acoustic signals, including emotional states such as joy, anger, sadness, and happiness.

[0549] "Performance attributes" refer to various elements used to adjust the texture and characteristics of the sound output, including tempo and echo.

[0550] A "correction value" is a value calculated to modify the sound output and is an indicator used to adjust pitch, tempo, and performance attributes.

[0551] "To alter" means to change or modify something from its original state.

[0552] "Reproduction" refers to outputting processed acoustic signals as physical sound.

[0553] This invention is a karaoke system that enhances the user's acoustic experience by processing acoustic signals in real time and extracting user emotional information from them, thereby providing natural and appropriate acoustic output.

[0554] First, the user selects a song using the device and begins singing into the microphone. The device then acquires the user's audio signal and converts the analog signal into a digital signal using a dedicated converter. The device then sends the converted digital signal to the server.

[0555] The server analyzes the received digital signal, extracting pitch and velocity, while simultaneously using an emotion engine to analyze emotional information in detail. Based on this emotional information, the server utilizes a generative AI model to calculate appropriate correction values ​​and dynamically modify performance attributes to adjust the acoustic output.

[0556] Ultimately, the server immediately sends the corrected audio signal back to the terminal, which then plays it back in real time through the speaker. This allows users and listeners to experience a natural, emotionally synchronized sound experience.

[0557] For example, if a user starts singing a pop song with high energy, the emotion engine senses the user's excitement. Based on this information, the server adjusts the tempo slightly, adds a light echo, and makes other adjustments. The resulting sound is tailored to the user's emotions, making karaoke more enjoyable.

[0558] An example of a prompt message would be, "Explain how to adjust the sound when the user has selected a pop song and is singing it with positive emotions."

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The user selects a song on the karaoke machine and begins singing. The terminal acquires the user's voice through the microphone and converts the analog audio signal into a digital signal. The input is an analog audio signal, and the output is the converted audio data. This conversion is performed using a digital audio converter (DAC).

[0562] Step 2:

[0563] The terminal transmits audio data to the server over the network. The input is digitized audio data, and the output is the data sent to the server. The data is packaged in an appropriate format and compression format.

[0564] Step 3:

[0565] The server analyzes the digital audio data it receives. The input is the digital audio data sent to the server, and the output is the analyzed pitch, velocity, and emotion information. The server uses speech recognition technology to extract pitch and velocity, and an emotion engine to analyze the emotion information.

[0566] Step 4:

[0567] The server uses a generative AI model to calculate correction values ​​based on the extracted emotional information. The inputs are pitch, velocity, and emotional information, and the output is the correction value necessary for adjusting the acoustic attributes. The AI ​​model learns from historical data and dynamically proposes appropriate correction measures.

[0568] Step 5:

[0569] The server prepares to modify the audio output by applying the calculated correction values. The input is the correction value, and the output is the corrected audio data. In this step, pitch shifting and formant adjustments are used to modify the sound while maintaining sound quality.

[0570] Step 6:

[0571] The server transmits the corrected audio data to the terminal. The input is the corrected audio data, and the output is the data transmitted to the terminal. Since the data is processed in real time, transmission is fast and efficient.

[0572] Step 7:

[0573] The device outputs corrected sound data received from the server through its speakers. The input is the corrected sound data received from the server, and the output is the sound that actually reaches the ears. In this step, the user and listener can enjoy natural sound that has been adjusted based on emotional information.

[0574] (Application Example 2)

[0575] Next, we will explain application example 2. In the following explanation, 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."

[0576] Conventional karaoke systems correct the pitch and tempo of the voice, but they could not take into account the user's emotional state. As a result, it was difficult for users to pursue a singing experience that fully reflected their emotions. The present invention aims to correct the voice according to the user's emotional state and provide a more moving and natural singing experience.

[0577] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0578] In this invention, the server includes means for acquiring audio data, means for converting the acquired audio data into a digital signal, means for analyzing the digital signal to extract pitch, tempo, and emotional state, means for calculating correction values ​​based on the emotional state, means for correcting the audio using the correction values, and means for outputting the corrected audio. This enables dynamic correction of the audio in accordance with the user's emotional state.

[0579] "Audio data" refers to data that represents an acoustic signal in digital format.

[0580] "Converting to a digital signal" refers to the process of converting an analog audio signal into a digital format.

[0581] "Pitch" refers to the element that indicates the high or low pitch of a sound.

[0582] "Tempo" refers to the speed of music or speech.

[0583] "Emotional state" refers to the emotional state extracted from the user's voice.

[0584] "Correction value" refers to a numerical setting used to adjust pitch, tempo, or effects.

[0585] "Correcting audio" is the process of adjusting the quality of audio by applying correction values.

[0586] "Output method" refers to a function for playing back the corrected audio using sound equipment or speakers.

[0587] "Past singing data" refers to the recorded data of musical performances accumulated to date.

[0588] A "learning model" is an algorithm that learns patterns from past data based on machine learning.

[0589] The system for implementing the present invention consists of three main elements: a terminal, a cloud server, and an audio device.

[0590] The terminal is envisioned to be a mobile information device such as a smartphone or tablet, which has the function of acquiring the user's voice and converting it into a digital signal. Voice input uses the microphone built into the terminal, the acoustic signal is converted into digital data, and it is transmitted to the server via the network.

[0591] The server analyzes the received digital audio data using audio analysis software. This software includes audio processing libraries such as Librosa, which are used to extract pitch, tempo, and emotional state. For emotion recognition, natural language processing APIs such as those provided by Google Cloud are used to recognize emotions from the audio. Based on the emotional state, the server uses a learning model to calculate correction values. This learning model is generated by analyzing past singing data, enabling voice adjustments that respond to the user's emotions. The SoX library is used to correct pitch and tempo, correcting the audio in real time.

[0592] The corrected audio data is retransmitted to the device and output through the device's speaker. This allows the user to experience an adjusted voice that matches their emotions in real time. For example, when a user sings a pop song with a "happy" feeling, the server recognizes the emotion, slightly speeds up the tempo, and adds appropriate effects to the voice. As a result, the user can enjoy karaoke even more.

[0593] Example prompt to input into the generative AI model: "Identify the user's emotions from this audio data and suggest appropriate sound effects."

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The user launches the karaoke app on their device and selects a song. The information for the selected song is loaded onto the device, and it is ready to sing. The user's voice is captured in real time via the microphone. The input is the user's voice, and the output is an analog audio signal.

[0597] Step 2:

[0598] The terminal converts the analog audio signal acquired by the microphone into digital data using a digital signal processing module. This digital data is then prepared for transmission to the server. The input is an analog audio signal, and the output is digital audio data.

[0599] Step 3:

[0600] The server receives digital audio data transmitted from the terminal. Using the Librosa library, it extracts pitch, tempo, and emotional state using an emotion recognition engine. The input is digital audio data, and the output is information on pitch, tempo, and emotional state.

[0601] Step 4:

[0602] The server calculates pitch and tempo correction values ​​based on the emotional state, using a learning model that utilizes past singing data. Furthermore, it uses the SoX library to perform real-time pitch shifting of the audio based on these correction values. The input is information on pitch, tempo, and emotional state, and the output is the corrected audio data.

[0603] Step 5:

[0604] The server sends the corrected audio data to the terminal. The terminal plays the received audio data through its speaker. This allows the user to hear emotion-adjusted audio in real time. The input is the corrected audio data, and the output is the audio output.

[0605] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0606] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0607] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] As shown in Figure 7, the 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.

[0611] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0612] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0613] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0614] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0615] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0616] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0617] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0618] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0619] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0620] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0621] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] The karaoke support system of the present invention improves the user's singing experience using speech recognition technology and speech correction technology.

[0623] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice from the microphone in real time and converts the audio into a digital signal. This audio data is then transmitted to a server via the network.

[0624] The server analyzes the received digital signal and extracts pitch and tempo from the audio. Next, it uses an AI model trained on past singing data to calculate the necessary correction values ​​from the extracted audio features. The server uses these correction values ​​to correct pitch deviations and tempo mismatches in real time. In particular, for pitch correction, it utilizes pitch shifting technology and formant adjustment to correct the voice quality without compromising it.

[0625] The corrected audio data is sent back to the device, which then outputs the audio through its speaker. This process allows the user and surrounding audience to enjoy a more harmonious and pleasant singing voice.

[0626] For example, when a user selects a children's song and begins to sing, if the pitch is off by a semitone, the server detects the discrepancy and calculates a correction value. By correcting the audio based on this value, the final sound output from the speaker will sound as if it were sung in the correct pitch. In this way, the system makes it possible to overcome tone-deafness and enjoy karaoke more.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone.

[0630] Step 2:

[0631] The device acquires the user's singing voice through the microphone and converts that analog signal into a digital signal.

[0632] Step 3:

[0633] The terminal transmits digital audio data acquired in real time to the server via the network.

[0634] Step 4:

[0635] The server applies a Short-Time Fourier Transform (STFT) to the received audio data to analyze and extract pitch and tempo.

[0636] Step 5:

[0637] The server inputs the analysis results into the AI ​​model and calculates correction values ​​using the model, which has been trained based on past singing data.

[0638] Step 6:

[0639] The server uses pitch shifting technology to correct the pitch of the user's voice and maintains voice quality by adjusting formants as needed.

[0640] Step 7:

[0641] The server adjusts for tempo discrepancies, generates corrected audio data at the appropriate timing, and sends it to the terminal.

[0642] Step 8:

[0643] The device outputs the corrected audio data in real time through its speaker, allowing the user and those around them to hear harmonious audio.

[0644] (Example 1)

[0645] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In audio recording and broadcasting, inaccurate pitch and tempo can create an unpleasant impression on listeners. This is especially true in situations requiring real-time audio output, such as karaoke, where correcting audio errors immediately is difficult. Therefore, there is a need to instantly correct pitch and tempo discrepancies to provide more natural-sounding audio.

[0647] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0648] In this invention, the server includes means for converting audio data into a digital signal, means for calculating correction values ​​for correcting pitch and tempo using a generation AI model, and means for correcting the audio by performing pitch shifting and formant adjustment. This makes it possible to provide more harmonious audio by correcting pitch and tempo errors in real time.

[0649] "Audio data" refers to information that is composed of sound in digital format and is used to represent acoustic signals.

[0650] A "digital signal" is a data format obtained by sampling and quantizing analog information, and is primarily suited for processing by electronic computers.

[0651] "Interval" refers to a characteristic that indicates the height of a sound, and it forms the basis of melody and chords in music.

[0652] "Tempo" refers to the speed of music and is a factor that determines the pace at which a song progresses.

[0653] A "correction value" is a numerical value calculated to correct errors or inconsistencies, and is used in audio processing to adjust pitch and tempo.

[0654] "Pitch shifting technology" is a technique that changes the pitch of an audio signal and is used to generate sounds of different pitches while maintaining sound quality.

[0655] "Formant adjustment" is a technique for changing the pitch of a voice without altering its quality, and is an acoustic processing method that maintains a natural-sounding voice.

[0656] A "generative AI model" is an artificial intelligence algorithm that uses learning based on past data to perform predictions and classifications.

[0657] The karaoke support system of the present invention uses speech recognition technology and speech correction technology to improve the user's singing experience. Specifically, it enables high-level real-time processing of the voice as the user sings on the karaoke machine.

[0658] The user selects a song on the karaoke machine and begins singing. The terminal captures the user's voice input from the microphone and converts the analog signal into a digital signal. This conversion needs to be highly accurate, and dedicated audio processing software is typically used. The terminal then transmits this digital audio data to a server via the network.

[0659] The server analyzes the received digital audio signal and extracts pitch and tempo from the audio. The extracted data is input into a generative AI model, and the AI, which has learned from past singing data, calculates correction values. Based on these correction values, the server corrects pitch deviations and tempo mismatches using pitch shifting technology and formant adjustment. Through this process, the corrected audio data is provided in real time as a more harmonious and natural-sounding voice.

[0660] The corrected audio data is sent back to the device, which then uses that data to output the corrected audio through its speakers. This technology allows users to check their own singing voice in real time while enjoying a comfortable music experience with those around them.

[0661] For example, if a user selects a "children's song" and starts singing, but the pitch is off, the server immediately detects the pitch discrepancy and calculates a correction value using a generative AI model. This correction value is then used to modify the audio, and the speaker outputs a sound that sounds like it's in the correct pitch.

[0662] An example of a prompt to a generative AI model would be, "Analyze the pitch and tempo of this audio data and calculate the necessary correction values." This prompt allows the AI ​​to properly analyze the audio data and apply the optimal corrections.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user selects a song they want to sing on the karaoke machine and begins singing into the microphone. The input at this time consists of the selected song information and the user's voice. The user's actions generate a physical acoustic signal from their singing voice.

[0666] Step 2:

[0667] The terminal converts the acoustic signal acquired from the microphone into a digital signal. The input is the user's natural voice (analog signal), and the output is digital audio data. The terminal uses an A / D conversion function to digitize the audio and format it for subsequent network transmission.

[0668] Step 3:

[0669] The terminal transmits digital audio data to the server over the network. The input is digital audio data, and the output is the audio data accurately transferred to the server. The terminal uses network protocols to deliver the data to the server quickly and reliably.

[0670] Step 4:

[0671] The server analyzes the received digital audio signal as pitch and tempo information. The input is digital audio data, and the output is pitch and tempo features. The server executes a signal processing algorithm to analyze the temporal and frequency characteristics of the audio.

[0672] Step 5:

[0673] The server inputs prompt text into the generating AI model, which then calculates correction values ​​to adjust pitch and tempo. The input is the audio features, and the output is the correction values. The server determines the optimal correction parameters through the calculation process performed by the AI ​​model.

[0674] Step 6:

[0675] The server uses the calculated correction values ​​to perform pitch shifting and formant adjustments to correct the audio. The input consists of the correction values ​​and the original audio data, while the output is the corrected audio data. The server utilizes advanced audio signal processing technology to adjust the pitch and tempo while maintaining audio quality.

[0676] Step 7:

[0677] The server sends the corrected audio data to the terminal, which then outputs it through its speaker. The input is the corrected audio data, and the output is clear audio delivered to the audience through the speaker. This allows the user to hear ideal singing audio in real time.

[0678] (Application Example 1)

[0679] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] To enhance the home music entertainment experience, the challenge lies in correcting pitch and tempo discrepancies when users sing along, thereby achieving a professional singing experience. In particular, there is a need to provide users with an easy way to enjoy high-quality music experiences by offering real-time corrected audio output using smartphones and augmented reality devices.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0682] In this invention, the server includes means for acquiring audio information and converting it into an information signal, means for extracting pitch and speed and calculating correction values, and means for outputting the corrected audio on an augmented reality device or a portable information terminal. This makes it possible for users to easily enjoy a professional-grade karaoke experience at home.

[0683] "Audio information" refers to all data related to sound, including digital information such as human voices and music.

[0684] An "information signal" is a signal that represents audio information in digital format and is used for data analysis and transmission.

[0685] "Pitch" is an indicator of the frequency of sound, and is used to evaluate the pitch of a person's singing voice or a song.

[0686] "Speed" is an indicator that shows the tempo or speed of audio playback, and is used to manage the rhythm of a song.

[0687] A "correction value" is a value calculated to correct deviations in pitch and tempo, and is used to bring the sound closer to the desired state.

[0688] An "augmented reality device" is a device that overlays digital information onto the real world, providing new experiences for sight and hearing.

[0689] "Portable information terminals" refer to all electronic devices that are easy to carry and capable of collecting, processing, and communicating information.

[0690] The system for carrying out the present invention mainly performs voice information acquisition, signal conversion, data analysis, correction processing, and voice output. A specific embodiment thereof is shown below.

[0691] First, the user acquires their own voice using an augmented reality device or a mobile device. Specifically, they collect singing voice using the device's voice input function. The acquired voice information is converted into an information signal in real time and then transmitted to a server via the network.

[0692] The server analyzes the received information signal and extracts pitch and speed. During this process, it utilizes a generative AI model and calculates correction values ​​using a correction model based on past singing data. The software used for this is, for example, TensorFlow, which allows for efficient data analysis and model inference.

[0693] The server then corrects the audio based on the calculated correction values. This correction process adjusts the pitch and corrects the tempo to a more natural level. The corrected audio is transmitted to an augmented reality device or mobile device and output to the user in real time. This allows the user to enjoy a professional singing experience from the comfort of their home.

[0694] As a concrete example, when a user sings the latest pop songs via their smartphone in their living room during the daytime, they can experience an augmented reality karaoke session where real-world sounds are blended with enhanced vocals. They can sing along with friends and family in real time, and the enhanced vocals create the desired harmony.

[0695] As an example of a prompt statement,

[0696] "We want to develop an app that corrects pitch and tempo in real time, allowing users to enjoy professional-sounding singing at home."

[0697] "Please suggest ways to improve the karaoke experience using smartphones."

[0698] These are some examples.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The device acquires the user's singing voice from the microphone. The input is an analog audio signal, and the output is digital audio information. The data processing performed by the device is the conversion of the analog audio signal into a digital signal. This makes the audio information ready for efficient transmission and analysis.

[0702] Step 2:

[0703] The server receives digital audio information transmitted from the terminal and analyzes its pitch and tempo. The input is digital audio information, and the output is pitch and tempo feature quantities. The data calculation performed by the server involves analyzing the pitch and tempo from the audio signal using the Fourier transform. This analysis allows the server to grasp the structural characteristics of the audio.

[0704] Step 3:

[0705] The server calculates a correction value using the analyzed pitch and tempo. The input is the pitch and tempo features, and the output is the correction value. The server uses a generative AI model to infer an appropriate correction value from a statistical model based on past singing data. This correction value allows for more accurate adjustment of the voice.

[0706] Step 4:

[0707] The server corrects the audio signal using calculated correction values. The input consists of digital audio information and correction values, while the output is the corrected audio information. The data processing performed by the server involves correcting pitch and speed using pitch shifting and time stretching techniques. This process maintains audio quality while performing the correction.

[0708] Step 5:

[0709] The device receives corrected audio information and outputs it to the user in real time. The input is corrected audio information, and the output is audio through the speaker. The device plays this corrected audio, creating a state where the user hears the adjusted singing voice. This allows the user to enjoy a seamless karaoke experience.

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] The karaoke support system of the present invention combines voice recognition technology, voice correction technology, and an emotion engine that recognizes the user's emotions to further improve the user's singing experience.

[0712] First, the user selects a song on the karaoke machine and begins singing into the microphone. The terminal acquires the user's singing voice through the microphone and converts the analog audio signal into a digital signal. This audio data is then transmitted to the server via the network.

[0713] The server analyzes the received digital signal and uses an emotion engine to extract pitch, tempo, and emotion. This emotion engine recognizes emotions such as joy, anger, sadness, and happiness from the user's voice.

[0714] Next, based on the emotional information analyzed by the emotion engine, processing is performed to dynamically adjust the pitch and tempo correction values. For example, if a positive emotion is recognized, effects such as slightly speeding up the tempo of the entire song or adding a subtle echo are applied.

[0715] The server uses an AI model trained on past singing data to calculate the necessary pitch correction values. Based on these correction values, pitch correction is performed using pitch shifting technology, and the voice is corrected while maintaining vocal quality through formant adjustment.

[0716] Finally, the corrected audio data is sent back to the device, which then outputs the audio through its speaker. As a result, the user and surrounding audience can experience naturally corrected audio that matches the user's emotions.

[0717] As a concrete example, suppose a user selects a "pop song" and begins singing with a very cheerful mood. The emotion engine recognizes the user's joy, and the server adds appropriate effects along with fine-tuning the tempo. The final output is tailored to the user's emotional state, making the karaoke experience even more enjoyable.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user selects their favorite song on the karaoke machine and begins singing into the microphone.

[0721] Step 2:

[0722] The device acquires the user's singing voice through the microphone and converts this analog audio signal into a digital signal.

[0723] Step 3:

[0724] The terminal transmits the converted digital audio data to the server in real time.

[0725] Step 4:

[0726] The server analyzes the received audio data and extracts pitch and tempo using the Short-Time Fourier Transform (STFT). It also uses an emotion engine to recognize the user's emotions based on the audio features.

[0727] Step 5:

[0728] Based on the analysis results from the emotion engine, the server calculates appropriate correction values ​​to adjust the pitch and tempo according to the user's emotions. For example, if joy is detected, it calculates a correction value to slightly increase the tempo.

[0729] Step 6:

[0730] Based on the calculated correction values, the server uses pitch shifting technology to correct the pitch and adjusts the formant to maintain sound quality while adjusting the voice to match the emotion.

[0731] Step 7:

[0732] The server adds emotion-based effects to the corrected audio data and sends the digital audio data to the terminal.

[0733] Step 8:

[0734] The device outputs corrected and enhanced audio through its speaker in real time, allowing the user and those around them to enjoy the sound.

[0735] (Example 2)

[0736] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0737] Conventional audio processing systems have struggled to adjust sound output while taking user emotions into account, resulting in an inability to achieve natural sound expression. Furthermore, the lack of technology to reflect user emotional information in real time made it difficult for users to have a satisfying singing experience.

[0738] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0739] In this invention, the server includes means for performing calculations to extract emotional information in addition to pitch and speed, means for calculating correction values ​​to adjust performance attributes based on the emotional information, and means for modifying the sound output by applying the correction values. This enables natural and real-time adjustment of the sound output in accordance with the user's emotions.

[0740] An "acoustic signal" is the waveform of an analog or digital sound before it is converted or processed.

[0741] A "digital signal" is a digital format obtained by converting a continuous analog signal into a digital format, which is a format used for processing by digital devices such as computers.

[0742] "Pitch" is an attribute based on the frequency of sound, and it is a characteristic that indicates the height of a sound.

[0743] "Speed" is an attribute that indicates the playback speed or tempo of a song.

[0744] "Emotional information" refers to emotional characteristics extracted from speech or acoustic signals, including emotional states such as joy, anger, sadness, and happiness.

[0745] "Performance attributes" refer to various elements used to adjust the texture and characteristics of the sound output, including tempo and echo.

[0746] A "correction value" is a value calculated to modify the sound output and is an indicator used to adjust pitch, tempo, and performance attributes.

[0747] "To alter" means to change or modify something from its original state.

[0748] "Reproduction" refers to outputting processed acoustic signals as physical sound.

[0749] This invention is a karaoke system that enhances the user's acoustic experience by processing acoustic signals in real time and extracting user emotional information from them, thereby providing natural and appropriate acoustic output.

[0750] First, the user selects a song using the device and begins singing into the microphone. The device then acquires the user's audio signal and converts the analog signal into a digital signal using a dedicated converter. The device then sends the converted digital signal to the server.

[0751] The server analyzes the received digital signal, extracting pitch and velocity, while simultaneously using an emotion engine to analyze emotional information in detail. Based on this emotional information, the server utilizes a generative AI model to calculate appropriate correction values ​​and dynamically modify performance attributes to adjust the acoustic output.

[0752] Ultimately, the server immediately sends the corrected audio signal back to the terminal, which then plays it back in real time through the speaker. This allows users and listeners to experience a natural, emotionally synchronized sound experience.

[0753] For example, if a user starts singing a pop song with high energy, the emotion engine senses the user's excitement. Based on this information, the server adjusts the tempo slightly, adds a light echo, and makes other adjustments. The resulting sound is tailored to the user's emotions, making karaoke more enjoyable.

[0754] An example of a prompt message would be, "Explain how to adjust the sound when the user has selected a pop song and is singing it with positive emotions."

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The user selects a song on the karaoke machine and begins singing. The terminal acquires the user's voice through the microphone and converts the analog audio signal into a digital signal. The input is an analog audio signal, and the output is the converted audio data. This conversion is performed using a digital audio converter (DAC).

[0758] Step 2:

[0759] The terminal transmits audio data to the server over the network. The input is digitized audio data, and the output is the data sent to the server. The data is packaged in an appropriate format and compression format.

[0760] Step 3:

[0761] The server analyzes the digital audio data it receives. The input is the digital audio data sent to the server, and the output is the analyzed pitch, velocity, and emotion information. The server uses speech recognition technology to extract pitch and velocity, and an emotion engine to analyze the emotion information.

[0762] Step 4:

[0763] The server uses a generative AI model to calculate correction values ​​based on the extracted emotional information. The inputs are pitch, velocity, and emotional information, and the output is the correction value necessary for adjusting the acoustic attributes. The AI ​​model learns from historical data and dynamically proposes appropriate correction measures.

[0764] Step 5:

[0765] The server prepares to modify the audio output by applying the calculated correction values. The input is the correction value, and the output is the corrected audio data. In this step, pitch shifting and formant adjustments are used to modify the sound while maintaining sound quality.

[0766] Step 6:

[0767] The server transmits the corrected audio data to the terminal. The input is the corrected audio data, and the output is the data transmitted to the terminal. Since the data is processed in real time, transmission is fast and efficient.

[0768] Step 7:

[0769] The device outputs corrected sound data received from the server through its speakers. The input is the corrected sound data received from the server, and the output is the sound that actually reaches the ears. In this step, the user and listener can enjoy natural sound that has been adjusted based on emotional information.

[0770] (Application Example 2)

[0771] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0772] Conventional karaoke systems correct the pitch and tempo of the voice, but they could not take into account the user's emotional state. As a result, it was difficult for users to pursue a singing experience that fully reflected their emotions. The present invention aims to correct the voice according to the user's emotional state and provide a more moving and natural singing experience.

[0773] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0774] In this invention, the server includes means for acquiring audio data, means for converting the acquired audio data into a digital signal, means for analyzing the digital signal to extract pitch, tempo, and emotional state, means for calculating correction values ​​based on the emotional state, means for correcting the audio using the correction values, and means for outputting the corrected audio. This enables dynamic correction of the audio in accordance with the user's emotional state.

[0775] "Audio data" refers to data that represents an acoustic signal in digital format.

[0776] "Converting to a digital signal" refers to the process of converting an analog audio signal into a digital format.

[0777] "Pitch" refers to the element that indicates the high or low pitch of a sound.

[0778] "Tempo" refers to the speed of music or speech.

[0779] "Emotional state" refers to the emotional state extracted from the user's voice.

[0780] "Correction value" refers to a numerical setting used to adjust pitch, tempo, or effects.

[0781] "Correcting audio" is the process of adjusting the quality of audio by applying correction values.

[0782] "Output method" refers to a function for playing back the corrected audio using sound equipment or speakers.

[0783] "Past singing data" refers to the recorded data of musical performances accumulated to date.

[0784] A "learning model" is an algorithm that learns patterns from past data based on machine learning.

[0785] The system for implementing the present invention consists of three main elements: a terminal, a cloud server, and an audio device.

[0786] The terminal is envisioned to be a mobile information device such as a smartphone or tablet, which has the function of acquiring the user's voice and converting it into a digital signal. Voice input uses the microphone built into the terminal, the acoustic signal is converted into digital data, and it is transmitted to the server via the network.

[0787] The server analyzes the received digital audio data using audio analysis software. This software includes audio processing libraries such as Librosa, which are used to extract pitch, tempo, and emotional state. For emotion recognition, natural language processing APIs such as those provided by Google Cloud are used to recognize emotions from the audio. Based on the emotional state, the server uses a learning model to calculate correction values. This learning model is generated by analyzing past singing data, enabling voice adjustments that respond to the user's emotions. The SoX library is used to correct pitch and tempo, correcting the audio in real time.

[0788] The corrected audio data is retransmitted to the device and output through the device's speaker. This allows the user to experience an adjusted voice that matches their emotions in real time. For example, when a user sings a pop song with a "happy" feeling, the server recognizes the emotion, slightly speeds up the tempo, and adds appropriate effects to the voice. As a result, the user can enjoy karaoke even more.

[0789] Example prompt to input into the generative AI model: "Identify the user's emotions from this audio data and suggest appropriate sound effects."

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] The user launches the karaoke app on their device and selects a song. The information for the selected song is loaded onto the device, and it is ready to sing. The user's voice is captured in real time via the microphone. The input is the user's voice, and the output is an analog audio signal.

[0793] Step 2:

[0794] The terminal converts the analog audio signal acquired by the microphone into digital data using a digital signal processing module. This digital data is then prepared for transmission to the server. The input is an analog audio signal, and the output is digital audio data.

[0795] Step 3:

[0796] The server receives digital audio data transmitted from the terminal. Using the Librosa library, it extracts pitch, tempo, and emotional state using an emotion recognition engine. The input is digital audio data, and the output is information on pitch, tempo, and emotional state.

[0797] Step 4:

[0798] The server calculates pitch and tempo correction values ​​based on the emotional state, using a learning model that utilizes past singing data. Furthermore, it uses the SoX library to perform real-time pitch shifting of the audio based on these correction values. The input is information on pitch, tempo, and emotional state, and the output is the corrected audio data.

[0799] Step 5:

[0800] The server sends the corrected audio data to the terminal. The terminal plays the received audio data through its speaker. This allows the user to hear emotion-adjusted audio in real time. The input is the corrected audio data, and the output is the audio output.

[0801] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0802] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0803] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0804] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0805] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0806] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0807] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0808] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0809] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0810] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0811] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0812] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0813] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0814] 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.

[0815] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0816] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0817] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0818] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0819] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0820] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0821] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] Means for acquiring audio data,

[0825] A means for converting acquired audio data into a digital signal,

[0826] A means for analyzing the aforementioned digital signal to extract pitch and tempo,

[0827] means for calculating correction values ​​for correcting the pitch and tempo,

[0828] A means for correcting the sound using the aforementioned correction value,

[0829] A means of outputting corrected audio,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the means for calculating correction values ​​for correcting pitch and tempo uses a correction model based on past singing data.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the means for outputting the corrected sound outputs the sound in real time.

[0835] "Example 1"

[0836] (Claim 1)

[0837] Means for acquiring audio data,

[0838] A means for converting acquired audio data into a digital signal,

[0839] A means for analyzing the aforementioned digital signal to extract pitch and tempo,

[0840] means for calculating correction values ​​for correcting the pitch and tempo,

[0841] A means for correcting the sound by performing pitch shifting and formant adjustment using the aforementioned correction value,

[0842] A means of outputting corrected audio,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the means for calculating correction values ​​for correcting pitch and tempo uses a generative AI model based on past audio data.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for outputting the corrected audio in real time.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] Means for acquiring audio information,

[0851] A means for converting acquired audio information into an information signal,

[0852] A means for analyzing the aforementioned information signal to extract pitch and speed,

[0853] A means for calculating correction values ​​for correcting the aforementioned pitch and speed,

[0854] A means for correcting the sound using the aforementioned correction value,

[0855] A means of outputting corrected audio,

[0856] A means for correcting pitch and speed and outputting audio on an augmented reality device or mobile information terminal,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the means for calculating the correction values ​​for correcting pitch and speed uses a correction model based on past singing information.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the means for outputting the corrected sound outputs the sound in real time and is usable as a home entertainment device.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] Means for acquiring acoustic signals,

[0865] A means for converting the acquired acoustic signal into a digital signal,

[0866] A means for analyzing the aforementioned digital signal to extract pitch and velocity,

[0867] In addition to the aforementioned pitch and tempo, means for performing calculations to extract emotional information,

[0868] means for calculating a correction value to adjust performance attributes based on the aforementioned emotional information,

[0869] A means for modifying the sound output by applying the aforementioned correction value,

[0870] A means of reproducing the modified audio output,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the means for calculating the correction value uses a generative model based on historical data.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the means for reproducing the modified sound output provides the sound output immediately.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] Means for acquiring audio data,

[0879] A means for converting acquired audio data into a digital signal,

[0880] A means for analyzing the aforementioned digital signal to extract pitch, tempo, and emotional state,

[0881] means for calculating a correction value based on the aforementioned emotional state,

[0882] A means for correcting the sound using the aforementioned correction value,

[0883] A means of outputting corrected audio,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the means for calculating the correction value based on the emotional state uses a learning model that utilizes past singing data.

[0887] (Claim 3)

[0888] The system according to claim 1, which outputs the corrected audio in real time. [Explanation of symbols]

[0889] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring audio data, A means for converting acquired audio data into a digital signal, A means for analyzing the aforementioned digital signal to extract pitch and tempo, means for calculating correction values ​​for correcting the aforementioned pitch and tempo, A means for correcting the sound using the aforementioned correction value, A means of outputting corrected audio, A system that includes this.

2. The system according to claim 1, wherein the means for calculating the correction values ​​for correcting the pitch and tempo uses a correction model based on past singing data.

3. The system according to claim 1, wherein the means for outputting the corrected sound outputs the sound in real time.

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