The implementation of modal processors, which involve the parallel combination resonant filters, may be costly for applications such as artificial reverberation that can require thousands of modes. In one embodiment, the input signal is decomposed into a plurality of subbands, the outputs of which are downsampled. In each downsampled band, resonant filters are applied at the downsampled sampling rate, and their output is upsampled and filtered to form the band output. In these and other embodiments, a feature of responses of the mode filters have been optimized to minimize an aspect of a residual error after a point in time.

The implementation of modal processors, which involve the parallel combination resonant filters, may be costly for applications such as artificial reverberation that can require thousands of modes. In one embodiment, the input signal is decomposed into a plurality of subbands, the outputs of which are downsampled. In each downsampled band, resonant filters are applied at the downsampled sampling rate, and their output is upsampled and filtered to form the band output. In these and other embodiments, a feature of responses of the mode filters have been optimized to minimize an aspect of a residual error after a point in time.

A musical piece generation device includes an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a data acquisition module configured to acquire target musical piece data indicating at least a part of a musical piece, a parameter acquisition module configured to acquire a value of a difficulty level parameter, a generation module configured to, by using a trained generative model, generate, from the target musical piece data and the value of the difficulty level parameter, new musical piece data indicating at least a part of a new musical piece obtained by changing a difficulty level of the musical piece to a difficulty level specified by the difficulty level parameter, and an output module configured to output the new musical piece data that has been generated.

A musical piece generation device includes an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a data acquisition module configured to acquire target musical piece data indicating at least a part of a musical piece, a parameter acquisition module configured to acquire a value of a difficulty level parameter, a generation module configured to, by using a trained generative model, generate, from the target musical piece data and the value of the difficulty level parameter, new musical piece data indicating at least a part of a new musical piece obtained by changing a difficulty level of the musical piece to a difficulty level specified by the difficulty level parameter, and an output module configured to output the new musical piece data that has been generated.

The present disclosure relates to a method of automatically preparing a MIDI file based on a target MIDI file comprising respective note information about each of a plurality of target notes and a source MIDI file comprising respective note information about each of a plurality of source notes. Each note information comprises pitch information defining a pitch of the note. The method comprises ranking the plurality of target notes based on the pitch of each target note. The method also comprises, for each of the ranked target notes, removing the pitch information from the note information of the target note. The method also comprises, for each of the ranked target notes, replacing the removed pitch information with pitch information of a corresponding source note, whereby the target note has the same pitch as the corresponding source note, forming a plurality of new notes of a new MIDI file.

The present disclosure relates to a method of automatically preparing a MIDI file based on a target MIDI file comprising respective note information about each of a plurality of target notes and a source MIDI file comprising respective note information about each of a plurality of source notes. Each note information comprises pitch information defining a pitch of the note. The method comprises ranking the plurality of target notes based on the pitch of each target note. The method also comprises, for each of the ranked target notes, removing the pitch information from the note information of the target note. The method also comprises, for each of the ranked target notes, replacing the removed pitch information with pitch information of a corresponding source note, whereby the target note has the same pitch as the corresponding source note, forming a plurality of new notes of a new MIDI file.

In general, the present invention relates to a method and system for synthesizing artificial reverberation using modal analysis of a room or resonating object. In one embodiment of the inventive system, a collection of resonant filters is employed, each driven by the source signal, and their outputs summed. With filter resonance frequencies and dampings tuned to the modal frequencies and decay times of the acoustic space or resonating object being simulated, and filter gains set according to the source and listener positions within the space or object, any number of acoustic spaces and resonant objects may be simulated.

In general, the present invention relates to a method and system for synthesizing artificial reverberation using modal analysis of a room or resonating object. In one embodiment of the inventive system, a collection of resonant filters is employed, each driven by the source signal, and their outputs summed. With filter resonance frequencies and dampings tuned to the modal frequencies and decay times of the acoustic space or resonating object being simulated, and filter gains set according to the source and listener positions within the space or object, any number of acoustic spaces and resonant objects may be simulated.

A method and system for generating music uses artificial intelligence to analyze existing musical compositions and then creates a musical composition that deviates from the learned styles. Known musical compositions created by humans are presented in digitized form along with a style designator to a computer for analysis, including recognition of musical elements and association of particular styles. A music generator generates a draft musical composition for similar analysis by the computer. The computer ranks such draft musical composition for correlation with known musical elements and known styles. The music generator modifies the draft musical composition using an iterative process until the resulting musical composition is recognizable as music but is distinctive in style.

A method and system for generating music uses artificial intelligence to analyze existing musical compositions and then creates a musical composition that deviates from the learned styles. Known musical compositions created by humans are presented in digitized form along with a style designator to a computer for analysis, including recognition of musical elements and association of particular styles. A music generator generates a draft musical composition for similar analysis by the computer. The computer ranks such draft musical composition for correlation with known musical elements and known styles. The music generator modifies the draft musical composition using an iterative process until the resulting musical composition is recognizable as music but is distinctive in style.