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 neural network is trained to digitally model a reference audio system. Training is carried out by repeatedly performing a set of operations. The set of operations includes predicting by the neural network, a model output based upon an input, where the output approximates an expected output of the reference audio system, and the prediction is carried out in the time domain. The set of operations also includes applying a perceptual loss function to the neural network based upon a determined psychoacoustic property, wherein the perceptual loss function is applied in the frequency domain. Moreover, the set of operations includes adjusting the neural network responsive to the output of the perceptual loss function. A neural model file is output that can be loaded to generate a virtualization of the reference audio system.

A system of generating a model for estimation is a system that generates the model for estimation used for estimation based on information about musical pieces, the system including: a learning data acquiring unit configured to acquire learning information in which information about audio data of a plurality of musical pieces having sequences is aligned in a dimension of the sequences in order of the sequences that is learning data used for machine learning; and a model generating unit configured to generate the model for estimation by performing machine learning using the learning information as an input to the model for estimation performing convolution using a kernel in the dimension of the sequences.

Provided is an information processing device including an acquisition unit that acquires subjective evaluation information from a second user about each performance performed by at least part of a body of a first user moving, a learning unit that performs machine learning on a relationship between the each performance and the corresponding subjective evaluation information and generates relationship information between the each performance and the corresponding subjective evaluation information, and a presentation unit that presents feedback information to the second user based on the relationship information.

Aspects of the subject disclosure may include, for example, receiving first audio content from a first communication device and receiving second audio content from a second communication device, and adjusting the first audio content and the second audio content. The adjusting of the first audio content and the second audio content can comprise: detecting a gap in the first audio content; analyzing the first audio content resulting in an audio analysis; generating filler audio content based on the audio analysis; and inserting the filler audio content into the gap of the first audio content. Further embodiments can include aggregating the first adjusted audio content with the second adjusted audio content resulting in aggregated audio content, and providing the aggregated audio content to a third communication device for playback. The third communication device plays the aggregated audio content. Other embodiments are disclosed.

An arrangement generation method executed by a computer includes acquiring target musical piece data that include performance information that indicates a melody and a chord of at least a part of a musical piece and include meta information that indicates characteristics of at least the part of the musical piece, generating, from the acquired target musical piece data, by using a generative model trained by machine learning, arrangement data obtained by arranging the performance information in accordance with the meta information, and outputting the generated arrangement data.

An estimation model construction method realized by a computer includes preparing a plurality of training data that include first training data that include first feature amount data that represent a first feature amount of a performance sound of a musical instrument and first onset data that represent a pitch at which an onset exists, and second training data that include second feature amount data that represent a second feature amount of sound generated by a sound source of a type different than the musical instrument, and second onset data that represent that an onset does not exist, and constructing, by machine learning using the plurality of training data, an estimation model that estimates, from a feature amount data that represent a feature amount of a performance sound of the musical instrument, estimated onset data that represent a pitch at which an onset exists.

Disclosed is a learning model generation method executed by a computer, including: striking a percussion instrument with a striking member to emit a musical sound; and conducting machine learning upon receiving an input of the musical sound emitted from the percussion instrument, and generating, based on the machine learning, a learning model for outputting numerical values for setting musical performance parameters for an automatic musical performance of the percussion instrument that is struck when the striking member is driven.

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 signal processing apparatus is provided that includes a sound source separation section configured to apply sound source separation processing to a mixed sound signal including a mixture of signals of a plurality of sound sources, and band extension sections configured to apply frequency band extension processing to respective sound source separation signals obtained by separation by the sound source separation section.