System

Abstract

An object of the system according to the embodiment is to create new music in which classical music and modern music culture are fused.SOLUTION: A system includes a classical music structure analysis unit, a modern music feature analysis unit, and a music generation unit. A classical music structure analysis part analyzes the structure of classical music. The modern music characteristics analyzer analyzes the characteristics of modern music. A music generation part generates new music obtained by fusing the structure of the classical musical piece analyzed by the classical musical piece structure analysis part and the characteristics of the modern music analyzed by the modern music characteristic analysis part.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 technology has not done enough to bridge the gap between classical music and modern music culture, and there is room for improvement.

[0005] The system according to the embodiment aims to create new music that combines classical music with modern music culture. [Means for solving the problem]

[0006] The system according to the embodiment includes a classical music structure analysis unit, a contemporary music feature analysis unit, and a music generation unit. The classical music structure analysis unit analyzes the structure of classical music. The contemporary music feature analysis unit analyzes the features of contemporary music. The music generation unit generates new music by combining the structure of classical music analyzed by the classical music structure analysis unit with the features of contemporary music analyzed by the contemporary music feature analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can create new music that combines classical music with modern music culture. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 music generation system according to an embodiment of the present invention analyzes the structure of classical music and generates new music that blends it with modern pop culture. This allows the music generation system to generate new music that blends classical music with modern music culture, reconstruct the value of classical music, and pass on its greatness to the next generation.

[0029] A music generation system according to an embodiment includes a classical music structure analysis unit, a contemporary music feature analysis unit, and a music generation unit. The classical music structure analysis unit analyzes the structure of classical music. For example, the classical music structure analysis unit analyzes music such as Beethoven's symphonies and Mozart's sonatas to extract elements such as melody, harmony, rhythm, and instrumentation. The classical music structure analysis unit also analyzes the structure of music in detail using a fine-tuned AI model. The contemporary music feature analysis unit analyzes the features of contemporary music. For example, the contemporary music feature analysis unit analyzes music in genres such as pop, rock, and hip-hop to understand contemporary music trends and styles. The contemporary music feature analysis unit also uses AI to receive contemporary music data as input and analyze its features. The music generation unit generates new music by combining the classical music structure analyzed by the classical music structure analysis unit with the features of contemporary music analyzed by the contemporary music feature analysis unit. For example, the music generation unit may combine classical melodies with pop rhythms or orchestral arrangements with electronic sounds. Furthermore, the music generation unit creates fusion music based on prompts, including instructions from the user. This allows the music generation system according to the embodiment to generate new music that combines classical music with modern music culture.

[0030] The classical music structure analysis unit can extract elements by taking into account the historical background of a piece of music and the composer's intentions. For example, when analyzing Beethoven's symphonies, the classical music structure analysis unit takes into account Beethoven's life and historical background to understand the intentions behind the melody and harmony of the piece. For example, it analyzes the "Fate" motive in Symphony No. 5. When analyzing Mozart's sonatas, the classical music structure analysis unit takes into account Mozart's compositional style and the music theory of the time to perform a detailed analysis of the piece's structure. For example, it extracts characteristics of the development section in sonata form. When analyzing Chopin's nocturnes, the classical music structure analysis unit takes into account Chopin's emotional expression and piano technique to understand the intentions behind the rhythm and dynamics of the piece. For example, it analyzes the ornaments in Nocturne No. 2. This allows the AI ​​to extract elements by taking into account the historical background of the piece and the composer's intentions.

[0031] The classical music structure analysis unit can compare performance data of the same piece by different performers and analyze differences in performance style. For example, the classical music structure analysis unit compares performance data of Beethoven's Symphony No. 9 by multiple conductors and analyzes differences in tempo and dynamics. For example, comparing performances by Karajan and Bernstein. The classical music structure analysis unit also compares performance data of Bach's Goldberg Variations by different pianists and analyzes differences in ornamentation and phrasing. For example, comparing performances by Gould and Perahia. The classical music structure analysis unit also compares performance data of Mozart's Piano Concerto by multiple orchestras and analyzes differences in orchestral arrangement and articulation. For example, comparing performances by the Vienna Philharmonic and the Berlin Philharmonic. This makes it possible to analyze differences in performance style of the same piece by different performers.

[0032] The classical music structure analysis section can be combined with structural analysis of other art forms to identify commonalities between different art forms. For example, the classical music structure analysis section compares the structure of Beethoven's Symphony No. 5 with contemporary paintings (such as Turner's landscapes) to identify common themes and expressive techniques. For example, it analyzes the repetition of motifs and color contrasts. The classical music structure analysis section also compares the structure of Chopin's nocturnes with contemporary literary works (such as Balzac's novels) to identify common emotional expressions and elements. For example, it analyzes emotional ups and downs and narrative development. The classical music structure analysis section also compares the structure of Mozart's operas with contemporary plays (such as Shakespeare's plays) to identify common dramatic elements and character portrayals. For example, it compares arias and monologues. This allows for the identification of commonalities between different art forms.

[0033] The classical music structure analysis unit can be used as interactive educational materials to help students visually understand the structure of a piece of music. For example, the classical music structure analysis unit could visualize the structure of Beethoven's Symphony No. 9 and develop teaching materials that allow students to interactively learn each part of the piece. For example, the themes and motives of each movement could be displayed color-coded. The classical music structure analysis unit could also visualize Mozart's sonata form and develop teaching materials that allow students to interactively learn the development and recapitulation sections of a piece of music. For example, the role and characteristics of each part could be shown using animation. The classical music structure analysis unit could also visualize the structure of Chopin's etudes and develop teaching materials that allow students to interactively learn technical elements and emotional expression. For example, fingering and dynamic changes could be displayed in graphs. This allows students to visually understand the structure of a piece of music.

[0034] The contemporary music feature analysis unit can incorporate social media trends or user feedback in real time. For example, when the AI ​​analyzes pop music trends, it collects hashtag data from Twitter and Instagram in real time to reflect the latest trends. For example, it identifies popular artists and songs. When the AI ​​analyzes hip-hop trends, it collects YouTube comments and play count data in real time to reflect user feedback. For example, it identifies lyrical themes and beat characteristics. When the AI ​​analyzes rock music trends, it collects Spotify playlist data in real time to reflect user preferences and play counts. For example, it identifies characteristics of guitar riffs and vocal styles. This allows it to incorporate social media trends and user feedback in real time.

[0035] The contemporary music feature analysis unit can integrate visual and musical elements, including data from music videos and live performances. For example, when the AI ​​analyzes the characteristics of pop music, it analyzes the visual elements of music videos (e.g., dance and visual effects) and integrates them with musical elements. For example, it analyzes the consistency of video cuts and rhythms. When the AI ​​analyzes the characteristics of rock music, it analyzes live performance data (e.g., staging and audience reactions) and integrates them with musical elements. For example, it analyzes guitar solos and vocal performances. When the AI ​​analyzes the characteristics of hip-hop music, it analyzes the visual elements of music videos (e.g., graffiti and street dance) and integrates them with musical elements. For example, it analyzes the consistency of lyric themes and visuals. This allows for the integration of visual and musical elements.

[0036] The contemporary music feature analysis unit can integrate visual and musical elements, including data from music videos and live performances. For example, when the AI ​​analyzes the characteristics of pop music, it analyzes the visual elements of music videos (e.g., dance and visual effects) and integrates them with musical elements. For example, it analyzes the consistency of video cuts and rhythms. When the AI ​​analyzes the characteristics of rock music, it analyzes live performance data (e.g., staging and audience reactions) and integrates them with musical elements. For example, it analyzes guitar solos and vocal performances. When the AI ​​analyzes the characteristics of hip-hop music, it analyzes the visual elements of music videos (e.g., graffiti and street dance) and integrates them with musical elements. For example, it analyzes the consistency of lyric themes and visuals. This allows for the integration of visual and musical elements.

[0037] The contemporary music feature analysis unit can also be applied to music from other entertainment fields, providing a cross-platform music experience. For example, the contemporary music feature analysis unit uses AI to analyze film music data to generate soundtracks incorporating elements of contemporary pop culture. For example, electronic music can be created to match scenes from action movies. The contemporary music feature analysis unit also uses AI to analyze game music data to generate background music incorporating elements of contemporary pop culture. For example, hip-hop beats can be created to match exploration scenes in open-world games. The contemporary music feature analysis unit also uses AI to analyze anime music data to generate opening themes incorporating elements of contemporary pop culture. For example, rock music can be created to match the opening theme of a battle anime. This allows the unit to be applied to music from other entertainment fields, providing a cross-platform music experience.

[0038] The contemporary music feature analysis unit can adapt to regional musical styles and incorporate diverse musical cultures from a global perspective. For example, AI can analyze African music data to generate new music incorporating elements of modern pop culture, such as fusing African beats with electronic music. Similarly, AI can analyze Asian music data to generate new music incorporating elements of modern pop culture, such as fusing K-POP rhythms with classical melodies. Similarly, AI can analyze Latin American music data to generate new music incorporating elements of modern pop culture, such as fusing salsa rhythms with hip-hop beats. This allows for adaptation to regional musical styles and the incorporation of diverse musical cultures from a global perspective.

[0039] The music generation unit can increase the diversity of music by trying different combinations of instruments or introducing new instruments. For example, when the AI ​​creates new music, it can combine classical string instruments with electronic synthesizers to generate diverse music. For example, it can combine a violin with a synth bass. When the AI ​​creates new music, it can also combine traditional folk instruments with modern pop instruments to generate diverse music. For example, it can combine a shakuhachi with an electric guitar. When the AI ​​creates new music, it can also introduce new instruments to generate diverse music. For example, it can combine digital instruments with acoustic instruments. This allows it to try different combinations of instruments or introduce new instruments to increase the diversity of music.

[0040] The music generation unit can link the creation of new music with other media and present it as a multimedia work. For example, the music generation unit can use AI to create new music and link it with a video work to present it as a multimedia work. For example, it can be used as a movie soundtrack. The music generation unit can also use AI to create new music and link it with an art installation to present it as a multimedia work. For example, it can be used for an exhibition in a gallery. The music generation unit can also use AI to create new music and link it with a game to present it as a multimedia work. For example, it can be used as background music for a game. In this way, the creation of new music can be linked with other media and presented as a multimedia work.

[0041] The music generation unit can customize the creation of new music to suit a specific event or theme, thereby providing a personalized music experience. For example, the music generation unit uses AI to create new music and customize it to suit a specific event (e.g., a wedding or a party). For example, the music generation unit generates romantic music to suit a wedding theme. The music generation unit can also use AI to create new music and customize it to suit a specific theme (e.g., a season or holiday). For example, the music generation unit can generate music to suit a Christmas theme. The music generation unit can also use AI to create new music and customize it to suit a specific user's preferences. For example, the music generation unit can generate music based on the user's favorite genre or artist. This allows the creation of new music to be customized to suit a specific event or theme, thereby providing a personalized music experience.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The music generation system may further include an environmental sound acquisition unit that acquires environmental sounds. The environmental sound acquisition unit may, for example, collect background sounds from the user's location in real time and adjust the music generation process based on the collected sounds. For example, if the user is in nature, the music generation unit may generate music incorporating the sounds of birds chirping and wind. Alternatively, if the user is in a city, the music generation unit may generate music incorporating the sounds of traffic and the hustle and bustle of people. This allows for a musical experience tailored to the user's environment.

[0044] The music generation system may further include an activity data acquisition unit that acquires user activity data. The activity data acquisition unit may, for example, monitor the user's exercise volume and travel distance in real time and adjust the music generation process based on that data. For example, if the user is running, the music generation unit may generate music with a fast tempo. On the other hand, if the user is relaxing, the music generation unit may generate music with a slower tempo. This allows for a musical experience tailored to the user's activity.

[0045] The music generation system may further include a history analysis unit that analyzes the user's past music history. The history analysis unit may, for example, collect data on songs the user has listened to in the past and adjust the music generation process based on that data. For example, it may generate music that incorporates the characteristics of genres and artists that the user has listened to in the past. It may also analyze the trends in songs the user listened to during a specific time period and generate music that is appropriate for that time period. This may provide a personalized music experience based on the user's past music history.

[0046] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0047] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0048] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The classical music structure analysis unit analyzes the structure of classical music. For example, it analyzes pieces such as Beethoven's symphonies and Mozart's sonatas to extract elements such as melody, harmony, rhythm, and instrumentation. It also uses fine-tuned AI models to perform a detailed analysis of the music's structure. Step 2: The contemporary music feature analysis unit analyzes the features of contemporary music. For example, it analyzes songs in genres such as pop, rock, and hip-hop to understand contemporary music trends and styles. The AI ​​also receives contemporary music data as input and analyzes its features. Step 3: The music generation unit generates new music by combining the classical music structure analyzed by the classical music structure analysis unit with the contemporary music characteristics analyzed by the contemporary music characteristics analysis unit. For example, classical melodies can be combined with pop rhythms, or orchestral arrangements can be combined with electronic sounds. The AI ​​also creates the combined music based on prompts, including instructions from the user.

[0051] (Example 2) The music generation system according to an embodiment of the present invention analyzes the structure of classical music and generates new music that blends it with modern pop culture. This allows the music generation system to generate new music that blends classical music with modern music culture, reconstruct the value of classical music, and pass on its greatness to the next generation.

[0052] A music generation system according to an embodiment includes a classical music structure analysis unit, a contemporary music feature analysis unit, and a music generation unit. The classical music structure analysis unit analyzes the structure of classical music. For example, the classical music structure analysis unit analyzes music such as Beethoven's symphonies and Mozart's sonatas to extract elements such as melody, harmony, rhythm, and instrumentation. The classical music structure analysis unit also analyzes the structure of music in detail using a fine-tuned AI model. The contemporary music feature analysis unit analyzes the features of contemporary music. For example, the contemporary music feature analysis unit analyzes music in genres such as pop, rock, and hip-hop to understand contemporary music trends and styles. The contemporary music feature analysis unit also uses AI to receive contemporary music data as input and analyze its features. The music generation unit generates new music by combining the classical music structure analyzed by the classical music structure analysis unit with the features of contemporary music analyzed by the contemporary music feature analysis unit. For example, the music generation unit may combine classical melodies with pop rhythms or orchestral arrangements with electronic sounds. Furthermore, the music generation unit creates fusion music based on prompts, including instructions from the user. This allows the music generation system according to the embodiment to generate new music that combines classical music with modern music culture.

[0053] The classical music structure analysis unit can extract elements by taking into account the historical background of a piece of music and the composer's intentions. For example, when analyzing Beethoven's symphonies, the classical music structure analysis unit takes into account Beethoven's life and historical background to understand the intentions behind the melody and harmony of the piece. For example, it analyzes the "Fate" motive in Symphony No. 5. When analyzing Mozart's sonatas, the classical music structure analysis unit takes into account Mozart's compositional style and the music theory of the time to perform a detailed analysis of the piece's structure. For example, it extracts characteristics of the development section in sonata form. When analyzing Chopin's nocturnes, the classical music structure analysis unit takes into account Chopin's emotional expression and piano technique to understand the intentions behind the rhythm and dynamics of the piece. For example, it analyzes the ornaments in Nocturne No. 2. This allows the AI ​​to extract elements by taking into account the historical background of the piece and the composer's intentions.

[0054] The classical music structure analysis unit can compare performance data of the same piece by different performers and analyze differences in performance style. For example, the classical music structure analysis unit compares performance data of Beethoven's Symphony No. 9 by multiple conductors and analyzes differences in tempo and dynamics. For example, comparing performances by Karajan and Bernstein. The classical music structure analysis unit also compares performance data of Bach's Goldberg Variations by different pianists and analyzes differences in ornamentation and phrasing. For example, comparing performances by Gould and Perahia. The classical music structure analysis unit also compares performance data of Mozart's Piano Concerto by multiple orchestras and analyzes differences in orchestral arrangement and articulation. For example, comparing performances by the Vienna Philharmonic and the Berlin Philharmonic. This makes it possible to analyze differences in performance style of the same piece by different performers.

[0055] The classical music structure analysis unit uses the emotion estimation function to analyze the emotional impact each part of a classical piece has on the audience, and can reevaluate the structure of the piece based on the emotional data. For example, the classical music structure analysis unit analyzes Beethoven's Symphony No. 6, "Pastoral," and estimates the emotional impact each movement has on the audience. For example, it compares the calm emotion of the first movement with the stormy emotion of the fourth movement. The classical music structure analysis unit also analyzes Chopin's Ballade No. 1 and estimates the emotional impact each part has on the audience. For example, it compares the sense of anxiety in the introduction with the sense of elation in the climax. The classical music structure analysis unit also analyzes Mozart's Requiem and estimates the emotional impact each part has on the audience. For example, it compares the sense of fear in "Die Irae" with the sadness in "Die Tears." This allows the emotional impact each part of a classical piece has on the audience to be analyzed, and the structure of the piece to be reevaluated based on the emotional data.

[0056] The classical music structure analysis section can be combined with structural analysis of other art forms to identify commonalities between different art forms. For example, the classical music structure analysis section compares the structure of Beethoven's Symphony No. 5 with contemporary paintings (such as Turner's landscapes) to identify common themes and expressive techniques. For example, it analyzes the repetition of motifs and color contrasts. The classical music structure analysis section also compares the structure of Chopin's nocturnes with contemporary literary works (such as Balzac's novels) to identify common emotional expressions and elements. For example, it analyzes emotional ups and downs and narrative development. The classical music structure analysis section also compares the structure of Mozart's operas with contemporary plays (such as Shakespeare's plays) to identify common dramatic elements and character portrayals. For example, it compares arias and monologues. This allows for the identification of commonalities between different art forms.

[0057] The classical music structure analysis unit can be used as interactive educational materials to help students visually understand the structure of a piece of music. For example, the classical music structure analysis unit could visualize the structure of Beethoven's Symphony No. 9 and develop teaching materials that allow students to interactively learn each part of the piece. For example, the themes and motives of each movement could be displayed color-coded. The classical music structure analysis unit could also visualize Mozart's sonata form and develop teaching materials that allow students to interactively learn the development and recapitulation sections of a piece of music. For example, the role and characteristics of each part could be shown using animation. The classical music structure analysis unit could also visualize the structure of Chopin's etudes and develop teaching materials that allow students to interactively learn technical elements and emotional expression. For example, fingering and dynamic changes could be displayed in graphs. This allows students to visually understand the structure of a piece of music.

[0058] The classical music structure analysis unit can use the emotion estimation function to generate a playlist that emphasizes parts of music that evoke specific emotions based on the results of the structural analysis of classical music. For example, the classical music structure analysis unit analyzes Beethoven's Symphony No. 6, "Pastoral," and generates a playlist that emphasizes parts that evoke calm emotions based on the emotion estimation data. For example, the first and fifth movements are selected. The classical music structure analysis unit can also analyze Chopin's Ballade No. 1 and generate a playlist that emphasizes parts that evoke elation based on the emotion estimation data. For example, the climax is selected. The classical music structure analysis unit can also analyze Mozart's Requiem and generate a playlist that emphasizes parts that evoke sadness based on the emotion estimation data. For example, "Day of Tears" and "Day of Wrath" are selected. This allows for the generation of a playlist that emphasizes parts of music that evoke specific emotions.

[0059] The contemporary music feature analysis unit can incorporate social media trends or user feedback in real time. For example, when the AI ​​analyzes pop music trends, it collects hashtag data from Twitter and Instagram in real time to reflect the latest trends. For example, it identifies popular artists and songs. When the AI ​​analyzes hip-hop trends, it collects YouTube comments and play count data in real time to reflect user feedback. For example, it identifies lyrical themes and beat characteristics. When the AI ​​analyzes rock music trends, it collects Spotify playlist data in real time to reflect user preferences and play counts. For example, it identifies characteristics of guitar riffs and vocal styles. This allows it to incorporate social media trends and user feedback in real time.

[0060] The contemporary music feature analysis unit can integrate visual and musical elements, including data from music videos and live performances. For example, when the AI ​​analyzes the characteristics of pop music, it analyzes the visual elements of music videos (e.g., dance and visual effects) and integrates them with musical elements. For example, it analyzes the consistency of video cuts and rhythms. When the AI ​​analyzes the characteristics of rock music, it analyzes live performance data (e.g., staging and audience reactions) and integrates them with musical elements. For example, it analyzes guitar solos and vocal performances. When the AI ​​analyzes the characteristics of hip-hop music, it analyzes the visual elements of music videos (e.g., graffiti and street dance) and integrates them with musical elements. For example, it analyzes the consistency of lyric themes and visuals. This allows for the integration of visual and musical elements.

[0061] The contemporary music feature analysis unit can integrate visual and musical elements, including data from music videos and live performances. For example, when the AI ​​analyzes the characteristics of pop music, it analyzes the visual elements of music videos (e.g., dance and visual effects) and integrates them with musical elements. For example, it analyzes the consistency of video cuts and rhythms. When the AI ​​analyzes the characteristics of rock music, it analyzes live performance data (e.g., staging and audience reactions) and integrates them with musical elements. For example, it analyzes guitar solos and vocal performances. When the AI ​​analyzes the characteristics of hip-hop music, it analyzes the visual elements of music videos (e.g., graffiti and street dance) and integrates them with musical elements. For example, it analyzes the consistency of lyric themes and visuals. This allows for the integration of visual and musical elements.

[0062] The contemporary music feature analysis unit uses an emotion estimation function to analyze the emotional impact that contemporary pop culture songs have on listeners, and can optimize the fusion process based on that data. For example, the contemporary music feature analysis unit uses AI to analyze pop music songs and identify the emotional impact that they have on listeners using the emotion estimation function. For example, it analyzes the sense of elation that the chorus of a song gives to the listener. The contemporary music feature analysis unit also uses AI to analyze hip-hop songs and identify the emotional impact that they have on listeners using the emotion estimation function. For example, it analyzes the empathy or anger that the content of the lyrics gives to the listener. The contemporary music feature analysis unit also uses AI to analyze rock music songs and identify the emotional impact that they have on listeners using the emotion estimation function. For example, it analyzes the excitement and energy that a guitar riff gives to the listener. This allows the contemporary music feature analysis unit to analyze the emotional impact that contemporary pop culture songs have on listeners, and can optimize the fusion process based on that data.

[0063] The contemporary music feature analysis unit can also be applied to music from other entertainment fields, providing a cross-platform music experience. For example, the contemporary music feature analysis unit uses AI to analyze film music data to generate soundtracks incorporating elements of contemporary pop culture. For example, electronic music can be created to match scenes from action movies. The contemporary music feature analysis unit also uses AI to analyze game music data to generate background music incorporating elements of contemporary pop culture. For example, hip-hop beats can be created to match exploration scenes in open-world games. The contemporary music feature analysis unit also uses AI to analyze anime music data to generate opening themes incorporating elements of contemporary pop culture. For example, rock music can be created to match the opening theme of a battle anime. This allows the unit to be applied to music from other entertainment fields, providing a cross-platform music experience.

[0064] The contemporary music feature analysis unit can adapt to regional musical styles and incorporate diverse musical cultures from a global perspective. For example, AI can analyze African music data to generate new music incorporating elements of modern pop culture, such as fusing African beats with electronic music. Similarly, AI can analyze Asian music data to generate new music incorporating elements of modern pop culture, such as fusing K-POP rhythms with classical melodies. Similarly, AI can analyze Latin American music data to generate new music incorporating elements of modern pop culture, such as fusing salsa rhythms with hip-hop beats. This allows for adaptation to regional musical styles and the incorporation of diverse musical cultures from a global perspective.

[0065] The contemporary music feature analysis unit can use the emotion estimation function to suggest musical styles that evoke specific emotions for the user based on the fusion results with modern pop culture. For example, the contemporary music feature analysis unit uses AI to analyze the fusion results of pop music and classical music, and then uses the emotion estimation function to suggest musical styles that evoke a sense of excitement for the user. For example, it can combine an up-tempo rhythm with a grand orchestra. The contemporary music feature analysis unit can also use AI to analyze the fusion results of hip-hop and classical music, and then uses the emotion estimation function to suggest musical styles that evoke a sense of relaxation for the user. For example, it can combine a smooth beat with a piano melody. The contemporary music feature analysis unit can also use AI to analyze the fusion results of rock music and classical music, and then uses the emotion estimation function to suggest musical styles that evoke a sense of energy for the user. For example, it can combine a powerful guitar riff with a dramatic string instrument. This allows the contemporary music feature analysis unit to suggest musical styles that evoke specific emotions for the user.

[0066] The music generation unit can analyze in detail the emotional nuances of each part of the generated music and optimize the emotional flow. For example, when the AI ​​creates new music, the music generation unit analyzes the emotional nuances of each part and optimizes the emotional flow. For example, it adjusts the emotional intensity from the intro to the climax. The music generation unit also uses an emotion estimation function to analyze the emotional nuances of each part and optimize the emotional flow. For example, it emphasizes the sense of elation in the chorus. The music generation unit also analyzes the emotional nuances of each part based on the emotion estimation data and optimizes the emotional flow. For example, it adjusts the tension in the bridge section. This allows the emotional nuances of each part of the generated music to be analyzed in detail and optimize the emotional flow.

[0067] The music generation unit can increase the diversity of music by trying different combinations of instruments or introducing new instruments. For example, when the AI ​​creates new music, it can combine classical string instruments with electronic synthesizers to generate diverse music. For example, it can combine a violin with a synth bass. When the AI ​​creates new music, it can also combine traditional folk instruments with modern pop instruments to generate diverse music. For example, it can combine a shakuhachi with an electric guitar. When the AI ​​creates new music, it can also introduce new instruments to generate diverse music. For example, it can combine digital instruments with acoustic instruments. This allows it to try different combinations of instruments or introduce new instruments to increase the diversity of music.

[0068] The music generation unit uses the emotion estimation function to monitor the emotional impact of the generated music on the listener in real time and improve the music based on the feedback. For example, when the AI ​​creates new music, the music generation unit uses the emotion estimation function to monitor the listener's emotional response in real time and improve the music based on the feedback. For example, emphasizing the listener's sense of elation. The music generation unit also monitors the listener's emotional response in real time based on the emotion estimation data and improves the music based on the feedback. For example, emphasizing the listener's sense of relaxation. The music generation unit also uses the emotion estimation function to monitor the listener's emotional response in real time and improves the music based on the feedback. For example, emphasizing the listener's sense of excitement. In this way, the emotional impact of the generated music on the listener can be monitored in real time and the music can be improved based on the feedback.

[0069] The music generation unit can link the creation of new music with other media and present it as a multimedia work. For example, the music generation unit can use AI to create new music and link it with a video work to present it as a multimedia work. For example, it can be used as a movie soundtrack. The music generation unit can also use AI to create new music and link it with an art installation to present it as a multimedia work. For example, it can be used for an exhibition in a gallery. The music generation unit can also use AI to create new music and link it with a game to present it as a multimedia work. For example, it can be used as background music for a game. In this way, the creation of new music can be linked with other media and presented as a multimedia work.

[0070] The music generation unit can customize the creation of new music to suit a specific event or theme, thereby providing a personalized music experience. For example, the music generation unit uses AI to create new music and customize it to suit a specific event (e.g., a wedding or a party). For example, the music generation unit generates romantic music to suit a wedding theme. The music generation unit can also use AI to create new music and customize it to suit a specific theme (e.g., a season or holiday). For example, the music generation unit can generate music to suit a Christmas theme. The music generation unit can also use AI to create new music and customize it to suit a specific user's preferences. For example, the music generation unit can generate music based on the user's favorite genre or artist. This allows the creation of new music to be customized to suit a specific event or theme, thereby providing a personalized music experience.

[0071] The music generation unit can use the emotion estimation function to continuously search for a musical style that elicits a specific emotion based on the user's emotional response to the generated music. For example, the music generation unit uses AI to create new music and the emotion estimation function to monitor the user's emotional response and search for a musical style that elicits a specific emotion. For example, it identifies a style that elicits a sense of elation in the listener. The music generation unit also monitors the user's emotional response based on the emotion estimation data and search for a musical style that elicits a specific emotion. For example, it identifies a style that elicits a sense of relaxation in the listener. The music generation unit also uses the emotion estimation function to monitor the user's emotional response and search for a style that elicits a specific emotion. For example, it identifies a style that elicits a sense of excitement in the listener. This allows for continuous searching for a musical style that elicits a specific emotion based on the user's emotional response to the generated music.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The music generation system may further include a biometric information acquisition unit that acquires biometric information of the user. The biometric information acquisition unit may, for example, monitor the user's heart rate and electrodermal response in real time and adjust the music generation process based on the user's physiological response. For example, if the user's heart rate increases, the music generation unit may emphasize a relaxing melody. Alternatively, if the user's electrodermal response increases, the music generation unit may incorporate an energetic rhythm. This may provide a more personalized music experience based on the user's physiological response.

[0074] The music generation system may further include an environmental sound acquisition unit that acquires environmental sounds. The environmental sound acquisition unit may, for example, collect background sounds from the user's location in real time and adjust the music generation process based on the collected sounds. For example, if the user is in nature, the music generation unit may generate music incorporating the sounds of birds chirping and wind. Alternatively, if the user is in a city, the music generation unit may generate music incorporating the sounds of traffic and the hustle and bustle of people. This allows for a musical experience tailored to the user's environment.

[0075] The music generation system may further include an activity data acquisition unit that acquires user activity data. The activity data acquisition unit may, for example, monitor the user's exercise volume and travel distance in real time and adjust the music generation process based on that data. For example, if the user is running, the music generation unit may generate music with a fast tempo. On the other hand, if the user is relaxing, the music generation unit may generate music with a slower tempo. This allows for a musical experience tailored to the user's activity.

[0076] The music generation system may further include a history analysis unit that analyzes the user's past music history. The history analysis unit may, for example, collect data on songs the user has listened to in the past and adjust the music generation process based on that data. For example, it may generate music that incorporates the characteristics of genres and artists that the user has listened to in the past. It may also analyze the trends in songs the user listened to during a specific time period and generate music that is appropriate for that time period. This may provide a personalized music experience based on the user's past music history.

[0077] The music generation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the music generation process based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or tone of voice to estimate the user's emotion. For example, if the user is happy, the music generation unit may generate a cheerful and happy piece of music. On the other hand, if the user is sad, the music generation unit may generate a calm piece of music. This makes it possible to provide a musical experience that corresponds to the user's emotion.

[0078] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0079] The music generation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the music generation process based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or tone of voice to estimate the user's emotion. For example, if the user is happy, the music generation unit may generate a cheerful and happy piece of music. On the other hand, if the user is sad, the music generation unit may generate a calm piece of music. This makes it possible to provide a musical experience that corresponds to the user's emotion.

[0080] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0081] The music generation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the music generation process based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's facial expression or tone of voice to estimate the user's emotion. For example, if the user is happy, the music generation unit may generate a cheerful and happy piece of music. On the other hand, if the user is sad, the music generation unit may generate a calm piece of music. This makes it possible to provide a musical experience that corresponds to the user's emotion.

[0082] The music generation system may further include a learning unit that learns the user's preferences. The learning unit may, for example, learn the characteristics of the music the user likes and adjust the music generation process based on those characteristics. For example, the learning unit may learn the user's preferred tempo and melody patterns and generate music incorporating those characteristics. The system may also learn the user's preferred instrument combinations and generate music incorporating those combinations. This allows the system to provide a musical experience tailored to the user's preferences.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The classical music structure analysis unit analyzes the structure of classical music. For example, it analyzes pieces such as Beethoven's symphonies and Mozart's sonatas to extract elements such as melody, harmony, rhythm, and instrumentation. It also uses fine-tuned AI models to perform a detailed analysis of the music's structure. Step 2: The contemporary music feature analysis unit analyzes the features of contemporary music. For example, it analyzes songs in genres such as pop, rock, and hip-hop to understand contemporary music trends and styles. The AI ​​also receives contemporary music data as input and analyzes its features. Step 3: The music generation unit generates new music by combining the classical music structure analyzed by the classical music structure analysis unit with the contemporary music characteristics analyzed by the contemporary music characteristics analysis unit. For example, classical melodies can be combined with pop rhythms, or orchestral arrangements can be combined with electronic sounds. The AI ​​also creates the combined music based on prompts, including instructions from the user.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 classical music structure analysis department that analyzes the structure of classical music; A contemporary music characteristics analysis department that analyzes the characteristics of contemporary music, a music generation unit that generates new music by combining the structure of classical music analyzed by the classical music structure analysis unit and the characteristics of modern music analyzed by the modern music characteristic analysis unit. A system characterized by:

2. The classical music structure analysis unit Extracting elements while taking into account the historical background of the music and the composer's intentions 2. The system of claim 1.

3. The classical music structure analysis unit Comparing performance data of the same piece by different performers and analyzing differences in performance style 2. The system of claim 1.

4. The classical music structure analysis unit Analyzing the emotional impact of each part of a classical piece of music on the listener and reevaluating the structure of the piece based on that emotional data 2. The system of claim 1.

5. The classical music structure analysis unit Combine with structural analysis of other art forms to find commonalities between different art forms 2. The system of claim 1.

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