Yamaha Corporation

Ryunosuke Daido

Jordi Bonada

Merlijn Blaauw

Hiraku Kayama

Yuji Hisaminato

Keijiro Saino

Masahiro Shimizu

Takashi Mori

Michael Wilson

Sumiya Ishikawa

Kanru Hua

A distribution system includes an obtaining circuit, a determination circuit, a generation circuit, and a distribution circuit. The obtaining circuit obtains reaction information from a terminal device of a viewer of the content. The reaction information indicates a reaction of the viewer to the content. The determination circuit determines whether the reaction information is first reaction information or second reaction information. The first reaction information indicates a first reaction made by equal to or more than a predetermined number of viewers. The second reaction information is different from the first reaction information and indicates a second reaction made by less than the predetermined number of viewers. The generation circuit generates an audience sound in a case of the first reaction information. The distribution circuit transmits the audience sound to the plurality of terminal devices.

Distribution system, distribution method, and non-transitory computer-readable recording medium

A computer-implemented method generates a frequency component vector of time series data, by executing a first process and a second process in each unit step. The first process includes: receiving first data; and processing the first data using a first neural network to generate intermediate data. The second process includes: receiving the generated intermediate data; and generating a plurality of component values corresponding to a plurality of frequency bands based on the generated intermediate data such that: a first component value corresponding to a first frequency band is generated using a second neural network based on the generated intermediate data; and a second component value corresponding to a second frequency band different from the first frequency band is generated using the second neural network based on the generated intermediate data and the generated first component value corresponding to the first frequency band.

Computer-implemented method and device for generating frequency component vector of time-series data

An information processing system includes at least one memory storing a program and at least one processor. The at least one processor implements the program to input a piece of sound source data obtained by encoding a first identification data representative of a sound source, a piece of style data obtained by encoding a second identification data representative of a performance style, and synthesis data representative of sounding conditions into a synthesis model generated by machine learning, and to generate, using the synthesis model, feature data representative of acoustic features of a target sound of the sound source to be generated in the performance style and according to the sounding conditions, and to generate an audio signal corresponding to the target sound using the generated feature data.

Information processing method and information processing system for sound synthesis utilizing identification data associated with sound source and performance styles

An information processing device includes a memory storing instructions, and a processor configured to implement the stored instructions to execute a plurality of tasks. The tasks includes: a first generating task that generates a series of fluctuations of a target sound based on first control data of the target sound to be synthesized, using a first model trained to have an ability to estimate a series of fluctuations of the target sound based on first control data of the target sound, and a second generating task that generates a series of features of the target sound based on second control data of the target sound and the generated series of fluctuations of the target sound, using a second model trained to estimate a series of features of the target sound based on second control data of the target sound and a series of fluctuations of the target sound.

Information processing method, estimation model construction method, information processing device, and estimation model constructing device

An audio processing system and a method thereof generate a synthesis model that can input an audio signal to generate feature data that can be used by a signal generator to generate a modified audio signal. Specifically, a pre-trained synthesis model is first generated using training audio data. Thereafter, a re-trained synthesis model is established by additionally training the pre-trained synthesis model. Based on a received instruction to modify at least one of sounding conditions of an audio signal to be processed, feature data is generated by inputting additional condition data into the re-trained synthesis model. The signal generator generates the modified audio signal from the generated feature data.

Audio processing method and audio processing system

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.

Sound processing method, sound processing apparatus, and recording medium

A computer-implemented sound signal generation method includes: obtaining a first sound source spectrum of a sound signal to be generated; obtaining a first spectral envelope of the sound signal; and estimating fragment data representative of samples of the sound signal based on the obtained first sound source spectrum and the obtained first spectral envelope.

Sound signal generation method, generative model training method, sound signal generation system, and recording medium

A method obtains a first sound signal representative of a first sound, including a first spectrum envelope contour and a first reference spectrum envelope contour; obtains a second sound signal, representative of a second sound differing in sound characteristics from the first sound, including a second spectrum envelope contour and a second reference spectrum envelope contour; generates a synthesis spectrum envelope contour by transforming the first spectrum envelope contour based on a first difference between the first spectrum envelope contour and the first reference spectrum envelope contour at a first time point of the first sound signal, and a second difference between the second spectrum envelope contour and the second reference spectrum envelope contour at a second time point of the second sound signal; and generates a third sound signal representative of the first sound that has been transformed using the generated synthesis spectrum envelope contour.

Voice synthesis method and apparatus generate second control data using an intermediate trained model with first input data including first control data designating phonetic identifiers, change the second control data in accordance with a first user instruction provided by a user, generate synthesis data representing frequency characteristics of a voice to be synthesized using a final trained model with final input data including the first control data and the changed second control data, and generate a voice signal based on the generated synthesis data.

Voice synthesis method, voice synthesis apparatus, and recording medium

A voice synthesis method according to an embodiment includes altering a series of synthesis spectra in a partial period of a synthesis voice based on a series of amplitude spectrum envelope contours of a voice expression to obtain a series of altered spectra to which the voice expression has been imparted, and synthesizing a series of voice samples to which the voice expression has been imparted, based on the series of altered spectra.

Voice synthesis method, voice synthesis device, and storage medium

A voice processing method realized by a computer includes compressing forward a first steady period of a plurality of steady periods in a voice signal representing voice, and extending forward a transition period between the first steady period and a second steady period of the plurality of steady periods in the voice signal. Each of the plurality of steady periods is a period in which acoustic characteristics are temporally stable. The second steady period is a period immediately after the first steady period and has a pitch that is different from a pitch of the first steady period.

Voice processing method for processing voice signal representing voice, voice processing device for processing voice signal representing voice, and recording medium storing program for processing voice signal representing voice

A voice synthesis method includes: supplying a first trained model with control data including phonetic identifier data to generate a series of frequency spectra of harmonic components; supplying a second trained model with the control data to generate a waveform signal representative of non-harmonic components; and generating a voice signal including the harmonic components and the non-harmonic components based on the series of frequency spectra of the harmonic components generated by the first trained model and the waveform signal representative of the non-harmonic components generated by the second trained model.

A voice synthesis method includes: sequentially acquiring voice units comprising at least one of diphone or a triphone in accordance with synthesis information for synthesizing voices; generating statistical spectral envelopes using a statistical model built by machine learning in accordance with the synthesis information for synthesizing the voices; and concatenating the sequentially acquired voice units and modifying a frequency spectral envelope of each voice unit in accordance with the generated statistical spectral envelope, thereby synthesizing a voice signal based on the concatenated voice units having the modified frequency spectra.

Voice synthesis apparatus and voice synthesis method utilizing diphones or triphones and machine learning

A voice synthesis method designates a target feature of a voice to be synthesized; specifies harmonic frequencies for a plurality of respective harmonic components of the voice and an amplitude spectrum envelope of the voice; specifies a harmonic amplitude distribution of each of the plurality of respective harmonic components based on (i) the target feature, (ii) the amplitude spectrum envelope, and (iii) the harmonic frequency specified for the respective harmonic component, the harmonic amplitude distribution representing a distribution of amplitudes in a unit band with a peak amplitude corresponding to the respective harmonic component; and generates a frequency spectrum of the voice with the target feature based on harmonic amplitude distributions specified for each of the plurality of respective harmonic components and the amplitude spectrum envelope.

A sound processing method includes a step of applying a nonlinear filter to a temporal sequence of spectral envelope of an acoustic signal, wherein the nonlinear filter smooths a fine temporal perturbation of the spectral envelope without smoothing out a large temporal change. A sound processing apparatus includes a smoothing processor configured to apply a nonlinear filter to a temporal sequence of spectral envelope of an acoustic signal, wherein the nonlinear filter smooths a fine temporal perturbation of the spectral envelope without smoothing out a large temporal change.

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Hamamatsu, Japan

Ryunosuke Daido