An information processing apparatus according to the present disclosure includes: a storage unit that stores a plurality of pieces of music feature information in which a plurality of types of feature amounts extracted from music information is associated with predetermined identification information, the music feature information being used as learning data in composition processing using machine learning; a reception unit that receives instruction information transmitted from a terminal apparatus; an extraction unit that extracts the music feature information from the storage unit according to the instruction information; and an output unit that outputs presentation information of the music feature information extracted by the extraction unit.

A driving sound library provides driving sounds classified in various themes to a user by analyzing frequency characteristics and temporal characteristics of sound sources classified into categories and determining a chord corresponding to each sound source based on the frequency characteristics and the time characteristics. Modulated sound sources in which each sound source is modulated are generated by applying a chord corresponding to each sound source. The driving sound sources are generated using the sound sources and the modulated sound sources as input data. Pitches of the driving sound sources are then changed based on the driving sound sources and engine sound orders corresponding to an engine RPM of a preset range. Scores for each theme for the driving sounds are generated.

A method and a system for synthesizing beat sound and music. The method comprises: selecting a sound material as a beat sound (S101); intelligently or manually regulating, according to a human body characteristic parameter, a beat sound frequency matched with the human body characteristic parameter (S102); selecting music (S103); and synthesizing the beat sound and the music in a volume ratio to obtain mixed music (S104). The method and system for synthesizing beat sound and music provided by the present disclosure can intelligently or manually regulate the beat sound frequency and the volume ratio of the beat sound and the music through human body characteristic parameter, and obtain the mixed music in various synthesis ways. The mixed music can be used in trainings for physical and mental relaxations and improving the concentration of the human body, and is suitable for practices of improving sleeping conditions of the human body, and improving practice effects of yoga, meditation, and zazen.

A music production system comprises: a computer interface comprising at least one input for receiving an external request for a piece of music and at least one output for transmitting a response to the external request which comprises or indicates a piece of music incorporating first music data; a first music production component configured to process second music data according to at least a first input setting so as to generate the first music data; a second music production component configured to receive via the computer interface an internal request, and provide the second music data based on at least a second input setting denoted by the internal request; and a controller configured to determine in response to the external request the first and second input settings, and instigate the internal request via the computer interface.

To provide database generation techniques that can accurately and efficiently generate a database useable in text-based sound signal search. A sound signal database generation apparatus includes: a latent variable generation unit that generates, from a sound signal, a latent variable corresponding to the sound signal using a sound signal encoder; a data generation unit that generates a natural language representation corresponding to the sound signal from the latent variable and a condition concerning an index for a natural language representation using a natural language representation decoder; and a sound signal database generation unit that generates a record including the natural language representation corresponding to the sound signal and the sound signal from the natural language representation corresponding to the sound signal and the sound signal, and generates a sound signal database made up of the record.

A computer-implemented method of generating a musical composition containing a plurality of musical texture groups is disclosed. The method includes assembling musical texture groups from musical instrument components and associating therewith a tag expressing emotional textural connotation. The instrument components have musical textural classifiers selected from a set of pre-defined textural classifiers such that different instrument components may have a different subset of pre-defined textural classifiers. The textural classifiers within a texture group possess either no musical feature attribute or a single musical feature attribute and any number of musical accompaniment attributes. The method then generates at least one chord scheme to a narrative brief, to provide an emotional connotation to a series of events, the chord scheme generated by selecting and assembling Form Atoms. The final step includes applying a texture to the chord scheme to generate the musical composition reflecting the narrative brief.

A generative composition system reduces existing musical artefacts to constituent elements termed “Form Atoms”. These Form Atoms may each be of varying length and have musical properties and associations that link together through Markov chains. To provide myriad new composition, a set of heuristics ensures that musical textures between concatenated musical sections follow a supplied and defined briefing narrative for the new composition whilst contiguous concatenated Form Atoms are also automatically selected to see that similarities in respective and identified attributes of musical textures for those musical sections are maintained to maintain good musical form. Within the composition work, chord spacing and control are practiced to maintain musical sense in the new composition and a primitive heuristics structure maintains pitch and permits key transformation. The system provides signal analysis and music generation by allowing emotional connotations to be specified and reproduced from cross-referenced Form-Atoms.

A generative composition system reduces existing musical artefacts to constituent elements termed “Form Atoms”. These Form Atoms may each be of varying length and have musical properties and associations that link together through Markov chains. To provide myriad new composition, a set of heuristics ensures that musical textures between concatenated musical sections follow a supplied and defined briefing narrative for the new composition whilst contiguous concatenated Form Atoms are also automatically selected to see that similarities in respective and identified attributes of musical textures for those musical sections are maintained to support maintenance of musical form. Independent aspects of the disclosure further ensure that, within the composition work, such as a media product or a real-time audio stream, chord spacing determination and control are practiced to maintain musical sense in the new composition. Further, a structuring of primitive heuristics operates to maintain pitch and permit key transformation. The system and its functionality provides signal analysis and music generation through allowing emotional connotations to be specified and reproduced from cross-referenced Form-Atoms.

A method of generating music contents from input music contents that includes development of models of music composition generation on the basis of business rules and composition rules. In parallel, sounds are prepared, which may be saved in the sound repository. Then, models in the form of source code are sent to a melody generator. Firstly, the generator is set with specific parameters using a controller conforming to MIDI standards and supplemented with composition characteristics read from the user preference database. Next, the contents are sent to automatic generation based on artificial intelligence algorithms and the digital score of the composition with the desired characteristics is generated. Sound tracks of individual instruments are rendered and the rendered tracks are mixed into the final music record. Next, the composition and its record are verified by the critic module using algorithms based on neural networks.

A computer implemented method is provided for generating a prediction of a next musical note by a computer having at least a processor and a memory. A computer processor system is also provided for generating a prediction of a next musical note. The method includes storing sequential musical notes in the memory. The method further includes generating, by the processor, the prediction of the next musical note based upon a music model and the sequential musical notes stored in the memory. The method also includes updating, by the processor, the music model based upon the prediction of the next musical note and an actual one of the next musical note. The method additionally includes resetting, by the processor, the memory at fixed time intervals.