Yamaha Corporation

National Institute of Advanced Industrial Science and Technology

Keita Arimoto

Takuya Fujishima

Masataka Goto

Sebastian Streich

Bee Suan Ong

Yuta Yuyama

Akira Miki

Daichi Watanabe

Kazuhiro Fujita

Masafumi Fuke

A performance support device includes a first surface, a second surface, and a flow path. The first surface is configured to face a blow hole of an air reed instrument. The second surface is configured to be displaced from the blow hole. The flow path penetrates from the first surface to the second surface and that is configured to flow an exhaled breath toward the blow hole. A cross-sectional area of the flow path at the first surface is smaller than a cross-sectional area of the flow path at a position displaced from the first surface toward the second surface.

Performance support device and air reed instrument

An apparatus for generating relations between feature amounts of audio and scene type includes at least one processor and a memory. The memory is operatively coupled to the at least one processor. The processor is configured to set one of the scene types to each of clusters classifying the feature amounts of audio in one or more pieces of content. The processor is also configured to generate a plurality of pieces of learning data, each representative of a feature amount, from among the feature amounts of the audio, that belongs to each cluster and the scene type set for each cluster. The processor is also configured to generate an identification model representative of relations between the feature amounts of audio and the scene types by performing machine learning using the plurality of pieces of learning data.

Apparatus for generating relations between feature amounts of audio and scene types and method therefor

An apparatus for generating relations between feature amounts of audio and scene types includes at least one processor and a memory. Upon execution of the instructions the processor is caused to set one of the scene types in accordance with an instruction from a user to indicate one of clusters classifying the feature amounts of audio in one or more pieces of content. The processor is also caused to generate a plurality of pieces of learning data, each representative of a feature amount, from among the feature amounts, that belongs to the cluster and the scene type set for the cluster. The processor is also caused to generate an identification model representative of relations between the feature amounts of audio and the scene types by performing machine learning using the plurality of pieces of learning data.

A recorded signal representing a recorded sound generated by a sound generation source is reproduced. A type of a sound generation source of a performance sound represented by a performance signal is specified. A sound volume of the recorded signal is reduced in a case where the sound generation source of the recorded signal corresponds to the specified type of the sound generation source.

Sound processing device and sound processing method

An audio processing device has an identification module and an adjustment information acquisition module. The identification module identifies each of the musical instruments that correspond to each of the audio signals. The adjustment information acquisition module acquires adjustment information for adjusting each of the audio signals described above according to the combination of the identified musical instruments.

Audio processing device and audio processing method

It is an object of the present invention to provide an improved technique for searching for a tone data set of a phrase constructed in a rhythm pattern that satisfies a predetermined condition of similarity to a rhythm pattern intended by a user. The user inputs a rhythm pattern via a rhythm input device. An input rhythm pattern storage section stores the input rhythm pattern into a RAM on the basis of clock signals output from a bar line clock output section and trigger data included in the input rhythm pattern. A rhythm pattern search section searches through a rhythm database for a tone data set presenting the highest degree of similarity to the stored input rhythm pattern. A performance processing section causes a sound output section to audibly output the searched-out tone data set.

Searching for a tone data set based on a degree of similarity to a rhythm pattern

In an audio analysis apparatus, a component acquirer acquires a component matrix composed of an array of component values, columns of the component matrix corresponding to the sequence of unit periods of an audio signal and rows of the component matrix corresponding to a series of unit bands of the audio signal arranged in a frequency-axis direction. A difference generator generates a plurality of shift matrices each obtained by shifting the columns of the component matrix in the time-axis direction with a different shift amount, and generates a plurality of difference matrices each composed of an array of element values in correspondence to the plurality of the shift matrices, the element value representing a difference between the corresponding component values of the shift matrix and the component matrix. A feature amount extractor generates a tonal feature amount including a plurality of series of feature values corresponding to the plurality of difference matrices, one series of feature values corresponding to the series of unit bands of the difference matrix, one feature value representing a sequence of element values arranged in the time-axis direction at the corresponding unit band of the difference matrix.

Audio analysis apparatus

Disclosed herein is a pitch estimation apparatus and associated methods for estimating a fundamental frequency of an audio signal from a fundamental frequency probability density function by modeling the audio signal as a weighted mixture of a plurality of tone models corresponding respectively to harmonic structures of individual fundamental frequencies, so that the fundamental frequency probability density function of the audio signal is given as a distribution of respective weights of the plurality of the tone models.

Estimating pitch by modeling audio as a weighted mixture of tone models for harmonic structures

Character value of a sound signal is extracted for each unit portion, and degrees of similarity between the character values of the individual unit portions are calculated and arranged in a matrix configuration. The matrix has arranged in each column the degrees of similarity acquired by comparing, for each of the unit portions, the sound signal and a delayed sound signal obtained by delaying the sound signal by a time difference equal to an integral multiple of a time length of the unit portion, and it has a plurality of the columns in association with different time differences. Repetition probability is calculated for each of the columns corresponding to the different time differences in the matrix. A plurality of peaks in a distribution of the repetition probabilities are identified. The loop region in the sound signal is identified by collating a reference matrix with the degree of similarity matrix.

Sound signal processing apparatus and method

In a musical analysis apparatus, a spectrum acquirer acquires a spectrum for each frame of an audio signal representing a piece of music. A beat specifier specifies a sequence of beats of the audio signal. A feature amount extractor divides an interval between the beats into a plurality of analysis periods such that one analysis period contains a plurality of frames, and separates the spectrum of the frames contained in one analysis period into a plurality of analysis bands so as to set a plurality of analysis units in one analysis period in correspondence with the plurality of the analysis bands, such that one analysis unit contains components of the spectrum belonging to the corresponding analysis band. The feature amount extractor further calculates a feature value of each analysis unit based on the components of the spectrum contained in each analysis unit, thereby generating a rhythmic feature amount that is an array of the feature values calculated for the analysis units and that features a rhythm of the piece of music.

Music analysis apparatus

A sound analysis apparatus employs tone models which are associated with various fundamental frequencies and each of which simulates a harmonic structure of a performance sound generated by a musical instrument, then defines a weighted mixture of the tone models to simulate frequency components of the performance sound, further sequentially updates and optimizes weight values of the respective tone models so that a frequency distribution of the weighted mixture of the tone models corresponds to a distribution of the frequency components of the performance sound, and estimates the fundamental frequency of the performance sound based on the optimized weight values.

Sound analysis apparatus and program

A sound analysis apparatus stores sound source structure data defining a constraint on one or more of sounds that can be simultaneously generated by a sound source of an input audio signal. A form estimation part selects fundamental frequencies of one or more of sounds likely to be contained in the input audio signal with peaked weights from various fundamental frequencies during sequential updating and optimizing of weights of tone models corresponding to the various fundamental frequencies, so that the sounds of the selected fundamental frequencies satisfy the sound source structure data, and creates form data specifying the selected fundamental frequencies. A previous distribution imparting part imparts a previous distribution to the weights of the tone models corresponding to the various fundamental frequencies so as to emphasize weights corresponding to the fundamental frequencies specified by the form data created by the form estimation part.

A similarity assessment apparatus is provided for assessing a performance sound based on a model performance sound. In the apparatus, a probability density function generating unit divides data of a performance sound into a sequence of frames each having a predetermined temporal length, and generates a probability density function of a fundamental frequency for each frame of the performance sound. A probability density function providing portion provides a probability density function of a fundamental frequency for each frame of the model performance sound. A similarity assessment unit compares the generated probability density function of a frame of the performance sound with the provided probability density function of a frame of the model performance sound so as to assess a similarity between the performance sound and the model performance sound.

Method, apparatus, and program for assessing similarity of performance sound

An electronic hi-hat cymbal comprises a hi-hat having a strike detector, a pedal unit having a stepped degree detector, a waveform data memory for storing a plurality of electronic hi-hat sound waveform data, corresponding to respective stepped degrees, in a plurality of stages, a CPU, a musical tone generating controller, and so forth. The CPU causes the musical tone generating controller to read out electronic hi-hat sound waveform data corresponding to a stepped degree in the pedal unit from the waveform data memory when a strike to the hi-hat is detected to thereby generate a musical tone signal before outputting, and when a change occurs to the stepped degree during a musical tone being produced thereafter, to read out electronic hi-hat sound waveform data corresponding to a new stepped degree halfway through to thereby generate a musical tone signal to be outputted, which is continued while the musical tone is being produced.

Electronic hi-hat cymbal

Respective waveform data on pad strike sounds, rim strike sounds, and rim-shot sounds are stored in a waveform memory, and when a CPU detects a strike detection signal outputted by either of a pad sensor, and a rim sensor, provided in a head unit, a musical tone generating controller reads out waveform data on an strike sound corresponding to the strike detection signal from the waveform memory, thereby generating a musical tone signal corresponding thereto before outputting. Thereafter, when the CPU detects a strike detection signal outputted by the other of the pad sensor, and the rim sensor within a predetermined time, waveform data on a rim-shot sound are read out from the waveform data memory, thereby generating a musical tone signal corresponding thereto before outputting. A sound output unit produces electronic strike sounds corresponding to those musical tone signals, respectively.

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Hamamatsu, Japan

Keita Arimoto