An audio processing method obtains observed envelopes of picked-up sound signals including a first observed envelope representing a contour of a first sound signal including a first target sound from a first sound source and a second spill sound from a second sound source and a second observed envelope representing a contour of a second sound signal including a second target sound from the second sound source and a first spill sound from the first sound source; and generates, based on the observed envelopes, output envelopes including a first output envelope representing a contour of the first target sound in the first observed envelope and a second output envelope representing a contour of the second target sound in the second observed envelope, using a mix matrix including a mix proportion of the second spill sound in the first sound signal and a mix proportion of the first spill sound in the second sound signal.

An audio signal analysis system comprises an electronic controller that is configured to execute a plurality of modules including an acquisition module configured to acquire a first spectrum, which is a time average of a plurality of frequency spectra of an audio signal, a specification module configured to acquire a plurality of reference values corresponding to different pitches that follow a prescribed temperament and configured to specify, by a problem-solving search algorithm, a frequency difference corresponding to a second spectrum which includes a plurality of components each having a frequency difference with respect to each of the plurality of reference values, the second spectrum being similar to the first spectrum with a degree of similarity exceeding a prescribed threshold value, and a correction module configured to correct the frequency difference so as to reduce systematic error included in the frequency difference specified by the specification module.

A method analyzes a reference signal representing a sound in each of analysis periods to obtain a series of phase spectra and a series of amplitude spectra over the analysis periods; adjusts, for each respective analysis period, phases of each harmonic component in a respective phase spectrum to be a target value at a pitch mark corresponding to the respective analysis period; synthesizes a first sound signal over the analysis periods based on the adjusted series of phase spectra of the reference signal and the obtained series of amplitude spectra of the reference signal; prepares training data including first control data representing of generative conditions of the reference signal and the first sound signal synthesized from the reference signal; and establishes, through machine learning using the training data, the generative model that generates a second sound signal based on second control data representing generative conditions of the second sound signal.

The present invention provides a method for processing audio data, comprising the steps of providing a first audio track of mixed input data, said mixed input data representing an audio signal containing a plurality of different timbres, decomposing the mixed input data to obtain decomposed data representing an audio signal containing at least one, but not all, of the plurality of different timbres, providing a second audio track, analyzing audio data, including at least the decomposed data, to determine at least one mixing parameter, and generating an output track based on the at least one mixing parameter, said output track comprising first output data obtained from the first audio track and second output data obtained from the second audio track.

The present invention relates to a method for processing and playing audio data comprising the steps of receiving mixed input data and playing recombined output data. Furthermore, the invention relates to a device for processing and playing audio data, preferably DJ equipment, comprising an audio input unit for receiving a mixed input signal, a recombination unit and a playing unit for playing recombined output data. In addition, the present invention relates to a method and a device for representing audio data, i.e. on a display.

The disclosure discloses a timbre fitting method and system based on time-varying multi-segment spectrum, the system includes an input device for obtaining audio signals of musical instruments and a segmented multi-model compensation module. The segmented multi-model compensation module learns a timbre of a source musical instrument and a target musical instrument, and establishes a multi-segment model of the sound feature of the source musical instrument and a multi-segment model of the sound feature of the target musical instrument. The sound feature is set to be based on maximum amplitude of the audio signal played the same sequence on the target musical instrument and the source musical instrument, and the audio signal of the sequence is divided into multiple segments according to the amplitude. The sound feature includes frequency spectrums of notes respectively within each amplitude range. The segmented multi-model compensation module establishes a multi-model structure with time-varying gain.

A sound processing method obtains note data representative of a note; obtains an audio signal to be processed; specifies, in accordance with the note, an expression sample representative of a sound expression to be imparted to the note and an expression period, of the audio signal, to which the sound expression is to be imparted to the note; and specifies, in accordance with the expression sample and the expression period, a processing parameter relating to an expression imparting processing for imparting the sound expression to a portion corresponding to the expression period in the audio signal. The method then generates a processed audio signal by performing the expression imparting processing in accordance with the expression sample, the expression period, and the processing parameter to the audio signal.

Apparatus, articles of manufacture, and systems for audio matching based on a harmonogram are disclosed. An example apparatus includes memory, and hardware to execute instructions to determine a first dominant frequency in a time slice of audio data based on a segment of a first spectrogram associated with the audio data, the first dominant frequency indicative of a first harmonic component of the time slice, determine a second dominant frequency indicative of a second harmonic component of the time slice, the second harmonic component less dominant than the first, generate a query harmonogram of the audio data, different segments of the query harmonogram representative of aggregate energy values of dominant frequencies in different time slices of the audio data, the dominant frequencies including at least one of the first or second dominant frequencies, and identify query sound based on a comparison of the query harmonogram to a reference harmonogram.

A sound processing device includes: a combining processor that combines a performance sound and a source sound, based on operation information corresponding to a performance operation on an instrument. The performance sound is obtained by picking up a sound generated by the performance operation on the instrument. The source sound is obtained from a sound source.

The disclosure discloses a timbre fitting method and system based on time-varying multi-segment spectrum, the system includes an input device for obtaining audio signals of musical instruments and a segmented multi-model compensation module. The segmented multi-model compensation module learns a timbre of a source musical instrument and a target musical instrument, and establishes a multi-segment model of the sound feature of the source musical instrument and a multi-segment model of the sound feature of the target musical instrument. The sound feature is set to be based on maximum amplitude of the audio signal played the same sequence on the target musical instrument and the source musical instrument, and the audio signal of the sequence is divided into multiple segments according to the amplitude. The sound feature includes frequency spectrums of notes respectively within each amplitude range. The segmented multi-model compensation module establishes a multi-model structure with time-varying gain.