System

Abstract

An object of a system according to an embodiment is to easily create a sound source for practice from which a specific instrument sound is removed.SOLUTION: A system according to an embodiment includes a music reading unit, an instrument sound removal unit, and a band sound source generation unit. The music reading unit reads a music file. The instrument sound removing unit removes an arbitrary instrument sound from the music file read by the music reading unit. The band sound source generation unit generates a sound source from which the instrument sound has been removed by the instrument sound removal unit as a band sound source for practice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to easily create a practice sound source from which specific instrument sounds have been removed.

[0005] The system according to the embodiment aims to easily create a practice sound source from which specific instrument sounds have been removed. [Means for solving the problem]

[0006] A system according to an embodiment includes a music loading unit, an instrument sound removal unit, and a band sound source generation unit. The music loading unit loads a music file. The instrument sound removal unit removes any instrument sounds from the music file loaded by the music loading unit. The band sound source generation unit generates a sound source from which the instrument sounds have been removed by the instrument sound removal unit as a band sound source for practice. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily create a practice sound source from which specific instrument sounds have been removed. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The generative AI system according to an embodiment of the present invention is a system that, when it receives music, removes the vocals and any instrumental sounds, such as guitar, piano, or drums, from the music and creates a band sound source for practice. This allows a user who is practicing an instrument alone to feel as if they are practicing with a band.

[0029] A generation AI system according to an embodiment includes a music loading unit, an instrument sound removal unit, and a band sound source generation unit. The music loading unit loads a music file. For example, it receives an MP3 or WAV format music file as input and separates the sounds of each instrument. The instrument sound removal unit removes any instrument sounds from the music file loaded by the music loading unit. For example, if a user instructs the generation AI to "remove the guitar sound," the generation AI detects the guitar sound and removes it. The instrument sound removal unit also outputs a sound source from which the drum sound has been removed, allowing the user to practice drums. The band sound source generation unit generates a practice band sound source from which the instrument sounds have been removed by the instrument sound removal unit. For example, the generation AI outputs the sound source from which the instrument sounds have been removed as a practice band sound source. This allows the generation AI system according to an embodiment to remove the instrument sounds specified by the user and generate a practice band sound source.

[0030] The music import unit can analyze not only the sounds of musical instruments, but also the characteristics of environmental sounds and live recordings. For example, when the generation AI loads a music file, the music import unit analyzes not only the sounds of musical instruments, but also the characteristics of environmental sounds and live recordings. For example, it analyzes the cheers of the audience and the reverberation of the venue that occur during live recordings, and generates a realistic sound source that includes these. This allows for the generation of a more realistic sound source.

[0031] The music reading unit can automatically generate musical score information when analyzing a music file, allowing the user to visually understand the structure of the music. For example, the music reading unit uses a generation AI to analyze a music file and automatically generate musical score information. For example, the unit analyzes notes and rhythm patterns and visually displays them, making it easier for the user to understand the structure of the music. This allows the user to visually understand the structure of the music.

[0032] The instrument sound removal unit can save the removed instrument sound as a separate track so that it can be reused later. For example, when the generation AI removes an instrument sound, the instrument sound removal unit saves the removed instrument sound as a separate track. For example, if a guitar sound is removed, the guitar sound is saved on a separate track so that it can be reused later. This allows the removed instrument sound to be reused later.

[0033] The instrument sound removal unit can customize the timbre of an instrument specified by the user. For example, when the generation AI removes instrument sounds, the instrument sound removal unit adds a function that allows the user to customize the timbre of an instrument specified by the user. For example, the unit allows the user to freely set the timbre of a guitar. This allows the user to customize the timbre of an instrument specified by the user.

[0034] The band sound source generation unit can adjust the difficulty level to match the user's playing skill. For example, when the generation AI creates a practice band sound source, the band sound source generation unit adjusts the difficulty level to match the user's playing skill. For example, the tempo can be slowed down for beginners, or complex parts can be simplified. This makes it possible to provide a practice band sound source that matches the user's playing skill.

[0035] The band sound source generation unit can provide real-time feedback on the user's performance and point out areas for improvement. For example, when the generation AI creates a band sound source for practice, the band sound source generation unit adds a function to provide real-time feedback on the user's performance. For example, it can point out discrepancies in timing or pitch and suggest areas for improvement. This allows the unit to provide real-time feedback on the user's performance and point out areas for improvement.

[0036] The digital amp + effects function can automatically adjust effect settings to suit the user's playing style. For example, when the generation AI provides the digital amp + effects function, it automatically adjusts effect settings to suit the user's playing style. For example, for rock style playing, it might set a stronger distortion. This allows the effect settings to be automatically adjusted to suit the user's playing style.

[0037] The digital amp + effector function can learn the user's past setting history and suggest optimal settings. For example, when the generation AI provides the digital amp + effector function, it learns the user's past setting history and suggests optimal effect settings. For example, it may prioritize suggestions of effects that the user uses frequently. This allows it to learn the user's past setting history and suggest optimal settings.

[0038] The digital amp + effects function can be applied to different instruments and vocals, making it suitable for a wide range of musical genres. For example, when the generative AI provides the digital amp + effects function, it adds the ability to apply it to different instruments and vocals. For example, it can support not only guitars, but also bass and keyboards. This allows it to be applied to different instruments and vocals, making it suitable for a wide range of musical genres.

[0039] The digital amplifier + effector function allows you to share effector settings with other users and collaborate on sound creation. For example, when the generation AI provides the digital amplifier + effector function, the digital amplifier + effector function adds a function to share effector settings with other users. For example, you can save the settings on the cloud and collaborate on sound creation with other users. This allows you to share effector settings with other users and collaborate on sound creation.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The music reading unit can automatically generate musical score information when analyzing a music file, allowing the user to visually understand the structure of the song. For example, the generation AI analyzes a music file and automatically generates musical score information. For example, by analyzing notes and rhythm patterns and visually displaying them, the user can easily understand the structure of the song. This allows the user to visually understand the structure of the song.

[0042] The instrument sound removal unit can save the removed instrument sounds as a separate track so that they can be reused later. For example, when the generation AI removes an instrument sound, it saves the removed instrument sound as a separate track. For example, if a guitar sound is removed, the guitar sound is saved as a separate track so that it can be reused later. This allows the removed instrument sound to be reused later.

[0043] The band sound source generation unit can adjust the difficulty level to match the user's playing skill. For example, when the generation AI creates a practice band sound source, it adjusts the difficulty level to match the user's playing skill. For example, it can slow down the tempo for beginners or simplify complex parts. This allows it to provide a practice band sound source that matches the user's playing skill.

[0044] The band sound generator can provide real-time feedback on the user's performance and point out areas for improvement. For example, when the generation AI creates a band sound source for practice, it can add a function that provides real-time feedback on the user's performance. For example, it can point out discrepancies in timing or pitch and suggest areas for improvement. This allows it to provide real-time feedback on the user's performance and point out areas for improvement.

[0045] The digital amplifier + effects function can automatically adjust effect settings to suit the user's playing style. For example, when the generation AI provides the digital amplifier + effects function, it automatically adjusts effect settings to suit the user's playing style. For example, it may set a stronger distortion for rock style playing. This allows the effect settings to be automatically adjusted to suit the user's playing style.

[0046] The digital amplifier + effects function allows users to share effects settings with other users and collaborate on sound creation. For example, when the generative AI provides the digital amplifier + effects function, it adds a function to share effects settings with other users. For example, the settings can be saved on the cloud and used to collaborate on sound creation. This allows users to share effects settings with other users and collaborate on sound creation.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The music reader reads the music file, e.g., MP3 or WAV format, and separates the sounds of each instrument. Step 2: The instrument sound removal unit removes any instrument sounds from the music file loaded by the music loading unit. For example, if the user instructs the AI ​​to "remove the guitar sound," the AI ​​will detect the guitar sound and remove it. The AI ​​can also output a sound source with the drum sounds removed, allowing the user to practice drumming. Step 3: The band sound source generator generates the sound source from which the instrument sounds have been removed by the instrument sound removal unit as a band sound source for practice. For example, the generation AI outputs the sound source from which any instrument sounds have been removed as a band sound source for practice.

[0049] (Example 2) The generative AI system according to an embodiment of the present invention is a system that, when it receives music, removes the vocals and any instrumental sounds, such as guitar, piano, or drums, from the music and creates a band sound source for practice. This allows a user who is practicing an instrument alone to feel as if they are practicing with a band.

[0050] A generation AI system according to an embodiment includes a music loading unit, an instrument sound removal unit, and a band sound source generation unit. The music loading unit loads a music file. For example, it receives an MP3 or WAV format music file as input and separates the sounds of each instrument. The instrument sound removal unit removes any instrument sounds from the music file loaded by the music loading unit. For example, if a user instructs the generation AI to "remove the guitar sound," the generation AI detects the guitar sound and removes it. The instrument sound removal unit also outputs a sound source from which the drum sound has been removed, allowing the user to practice drums. The band sound source generation unit generates a practice band sound source from which the instrument sounds have been removed by the instrument sound removal unit. For example, the generation AI outputs the sound source from which the instrument sounds have been removed as a practice band sound source. This allows the generation AI system according to an embodiment to remove the instrument sounds specified by the user and generate a practice band sound source.

[0051] The music import unit can analyze not only the sounds of musical instruments, but also the characteristics of environmental sounds and live recordings. For example, when the generation AI loads a music file, the music import unit analyzes not only the sounds of musical instruments, but also the characteristics of environmental sounds and live recordings. For example, it analyzes the cheers of the audience and the reverberation of the venue that occur during live recordings, and generates a realistic sound source that includes these. This allows for the generation of a more realistic sound source.

[0052] The music reading unit can automatically generate musical score information when analyzing a music file, allowing the user to visually understand the structure of the music. For example, the music reading unit uses a generation AI to analyze a music file and automatically generate musical score information. For example, the unit analyzes notes and rhythm patterns and visually displays them, making it easier for the user to understand the structure of the music. This allows the user to visually understand the structure of the music.

[0053] The music loading unit can use the emotion estimation function to analyze the emotional tone of a song and generate a sound source that matches the emotion desired by the user. For example, the music loading unit uses a generation AI to analyze a music file and analyzes the emotional tone of the song using the emotion estimation function. For example, the unit can estimate emotions from the tempo and melody line of the song and generate a sound source that matches the emotion desired by the user. This makes it possible to generate a sound source that matches the emotion desired by the user.

[0054] The instrument sound removal unit can save the removed instrument sound as a separate track so that it can be reused later. For example, when the generation AI removes an instrument sound, the instrument sound removal unit saves the removed instrument sound as a separate track. For example, if a guitar sound is removed, the guitar sound is saved on a separate track so that it can be reused later. This allows the removed instrument sound to be reused later.

[0055] The instrument sound removal unit can customize the timbre of an instrument specified by the user. For example, when the generation AI removes instrument sounds, the instrument sound removal unit adds a function that allows the user to customize the timbre of an instrument specified by the user. For example, the unit allows the user to freely set the timbre of a guitar. This allows the user to customize the timbre of an instrument specified by the user.

[0056] The instrument sound removal unit uses the emotion estimation function to analyze the user's emotion regarding the instrument sound they want to remove, and can propose an optimal removal method based on that emotion. For example, when the generation AI removes instrument sounds, the instrument sound removal unit uses the emotion estimation function to analyze the user's emotion. For example, it analyzes the user's emotion regarding the instrument sound they want to remove, and proposes an optimal removal method based on that emotion. This makes it possible to propose an optimal removal method based on the user's emotion.

[0057] The band sound source generation unit can adjust the difficulty level to match the user's playing skill. For example, when the generation AI creates a practice band sound source, the band sound source generation unit adjusts the difficulty level to match the user's playing skill. For example, the tempo can be slowed down for beginners, or complex parts can be simplified. This makes it possible to provide a practice band sound source that matches the user's playing skill.

[0058] The band sound source generation unit can provide real-time feedback on the user's performance and point out areas for improvement. For example, when the generation AI creates a band sound source for practice, the band sound source generation unit adds a function to provide real-time feedback on the user's performance. For example, it can point out discrepancies in timing or pitch and suggest areas for improvement. This allows the unit to provide real-time feedback on the user's performance and point out areas for improvement.

[0059] The band sound source generation unit uses the emotion estimation function to analyze the emotions felt by the user during practice and customizes the practice sound source based on those emotions. For example, when the generation AI creates a band sound source for practice, the band sound source generation unit uses the emotion estimation function to analyze the user's emotions. For example, it analyzes the stress or enjoyment felt by the user during practice and customizes the sound source based on those emotions. This allows the practice sound source to be customized based on the user's emotions.

[0060] The digital amp + effects function can automatically adjust effect settings to suit the user's playing style. For example, when the generation AI provides the digital amp + effects function, it automatically adjusts effect settings to suit the user's playing style. For example, for rock style playing, it might set a stronger distortion. This allows the effect settings to be automatically adjusted to suit the user's playing style.

[0061] The digital amp + effector function can learn the user's past setting history and suggest optimal settings. For example, when the generation AI provides the digital amp + effector function, it learns the user's past setting history and suggests optimal effect settings. For example, it may prioritize suggestions of effects that the user uses frequently. This allows it to learn the user's past setting history and suggest optimal settings.

[0062] The digital amp + effects function uses an emotion estimation function to analyze the user's emotions when using the effects, and can customize effect settings based on those emotions. For example, when the generative AI provides the digital amp + effects function, the digital amp + effects function uses the emotion estimation function to analyze the user's emotions. For example, the digital amp + effects function analyzes the user's emotions when using the effects, and customizes effect settings based on those emotions. This makes it possible to customize effect settings based on the user's emotions.

[0063] The digital amp + effects function can be applied to different instruments and vocals, making it suitable for a wide range of musical genres. For example, when the generative AI provides the digital amp + effects function, it adds the ability to apply it to different instruments and vocals. For example, it can support not only guitars, but also bass and keyboards. This allows it to be applied to different instruments and vocals, making it suitable for a wide range of musical genres.

[0064] The digital amplifier + effector function allows you to share effector settings with other users and collaborate on sound creation. For example, when the generation AI provides the digital amplifier + effector function, the digital amplifier + effector function adds a function to share effector settings with other users. For example, you can save the settings on the cloud and collaborate on sound creation with other users. This allows you to share effector settings with other users and collaborate on sound creation.

[0065] The Digital Amplifier + Effector function uses an emotion estimation function to analyze the user's emotions in real time when using the Digital Amplifier + Effector function, and can suggest effect settings based on those emotions. For example, when the generative AI provides the Digital Amplifier + Effector function, the Digital Amplifier + Effector function uses the emotion estimation function to analyze the user's emotions in real time. For example, it can estimate emotions from the user's facial expressions and voice and suggest effect settings based on those emotions. This makes it possible to suggest effect settings based on the user's emotions.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The music reading unit can automatically generate musical score information when analyzing a music file, allowing the user to visually understand the structure of the song. For example, the generation AI analyzes a music file and automatically generates musical score information. For example, by analyzing notes and rhythm patterns and visually displaying them, the user can easily understand the structure of the song. This allows the user to visually understand the structure of the song.

[0068] The music reading unit can use the emotion estimation function to analyze the emotional tone of a song and generate a sound source that matches the emotion desired by the user. For example, the generation AI analyzes a music file and uses the emotion estimation function to analyze the emotional tone of the song. For example, it can estimate emotions from the tempo and melody line of the song and generate a sound source that matches the emotion desired by the user. This makes it possible to generate a sound source that matches the emotion desired by the user.

[0069] The instrument sound removal unit can save the removed instrument sounds as a separate track so that they can be reused later. For example, when the generation AI removes an instrument sound, it saves the removed instrument sound as a separate track. For example, if a guitar sound is removed, the guitar sound is saved as a separate track so that it can be reused later. This allows the removed instrument sound to be reused later.

[0070] The instrument sound removal unit uses the emotion estimation function to analyze the user's emotion regarding the instrument sound they want to remove, and can propose the optimal removal method based on that emotion. For example, when the generation AI removes instrument sounds, it uses the emotion estimation function to analyze the user's emotion. For example, it analyzes the user's emotion regarding the instrument sound they want to remove, and proposes the optimal removal method based on that emotion. This makes it possible to propose the optimal removal method based on the user's emotion.

[0071] The band sound source generation unit can adjust the difficulty level to match the user's playing skill. For example, when the generation AI creates a practice band sound source, it adjusts the difficulty level to match the user's playing skill. For example, it can slow down the tempo for beginners or simplify complex parts. This allows it to provide a practice band sound source that matches the user's playing skill.

[0072] The band sound generator can provide real-time feedback on the user's performance and point out areas for improvement. For example, when the generation AI creates a band sound source for practice, it can add a function that provides real-time feedback on the user's performance. For example, it can point out discrepancies in timing or pitch and suggest areas for improvement. This allows it to provide real-time feedback on the user's performance and point out areas for improvement.

[0073] The band sound source generation unit uses the emotion estimation function to analyze the emotions felt by the user during practice and customizes the practice sound source based on those emotions. For example, when the generation AI creates a band practice sound source, it uses the emotion estimation function to analyze the user's emotions. For example, it analyzes the stress or enjoyment felt by the user during practice and customizes the sound source based on those emotions. This allows the practice sound source to be customized based on the user's emotions.

[0074] The digital amplifier + effects function can automatically adjust effect settings to suit the user's playing style. For example, when the generation AI provides the digital amplifier + effects function, it automatically adjusts effect settings to suit the user's playing style. For example, it may set a stronger distortion for rock style playing. This allows the effect settings to be automatically adjusted to suit the user's playing style.

[0075] The digital amplifier + effects function uses an emotion estimation function to analyze the user's emotions when using the effects pedal, and can customize effect settings based on those emotions. For example, when the generative AI provides the digital amplifier + effects function, it uses the emotion estimation function to analyze the user's emotions. For example, it analyzes the user's emotions when using the effects pedal, and customizes effect settings based on those emotions. This allows effect settings to be customized based on the user's emotions.

[0076] The digital amplifier + effects function allows users to share effects settings with other users and collaborate on sound creation. For example, when the generative AI provides the digital amplifier + effects function, it adds a function to share effects settings with other users. For example, the settings can be saved on the cloud and used to collaborate on sound creation. This allows users to share effects settings with other users and collaborate on sound creation.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The music reader reads the music file, e.g., MP3 or WAV format, and separates the sounds of each instrument. Step 2: The instrument sound removal unit removes any instrument sounds from the music file loaded by the music loading unit. For example, if the user instructs the AI ​​to "remove the guitar sound," the AI ​​will detect the guitar sound and remove it. The AI ​​can also output a sound source with the drum sounds removed, allowing the user to practice drumming. Step 3: The band sound source generator generates the sound source from which the instrument sounds have been removed by the instrument sound removal unit as a band sound source for practice. For example, the generation AI outputs the sound source from which any instrument sounds have been removed as a band sound source for practice.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] 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.

[0138] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0139] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a music reading unit that reads music files; an instrument sound removal unit that removes any instrument sound from the music file read by the music reading unit; a band sound source generating unit that generates a sound source from which the instrument sound has been removed by the instrument sound removing unit as a band sound source for practice. A system characterized by:

2. The music reading unit Analyzing not only the sounds of musical instruments but also the characteristics of environmental sounds and live recordings 2. The system of claim 1.

3. The instrument sound removal unit Save the removed instrument as a separate track for future reuse 2. The system of claim 1.

4. The band sound source generation unit Adjust the difficulty level to suit the user's playing skill 2. The system of claim 1.

5. Digital amplifier + effector function Automatically adjusts effect settings to suit the user's playing style 2. The system of claim 1.

6. The music reading unit Analyzes the emotional tone of a song using emotion estimation functionality and generates audio that matches the emotion desired by the user.

2. The system of claim 1.

Images