Yamaha Corporation

National Institute of Advanced Industrial Science and Technology

Tokyo University of the Arts

Takuya Fujishima

Jordi Bonada

Akira Maezawa

Keita Arimoto

Sebastian Streich

Motoichi Tamura

Masataka Goto

Norihiro Uemura

Motoji Nagata

Maarten De Boer

Yoshihiro Katsumata

Yasuyuki Umeyama

Kazuki Kashiwase

Daichi Watanabe

Kouhei Sumi

Bee Suan Ong

Shinya Sakurada

Takeshi Kikuchi

Katsumi Ishikawa

Alex Loscos

Kiyohisa Sugii

Toshiyuki Hata

Takashi Kiriyama

Dan Sasai

Shingo Kawasaki

Kenichi Ohya

Oscar Mayor

Samuel Roig

Ryoya Usuha

Noriko Koshida

Fokke De Jong

Takashi Otsuka

Naoaki Kojima

Toshiyuki Kuwabara

Teruyo Uehira

A signal processing device includes an electronic controller including at least one processor. The electronic controller is configured to execute a reception unit, a generation unit, and a processing unit. The reception unit is configured to receive first time-series data that include sound data, and second time-series data that are generated based on the first time-series data and that include at least data indicating a timing of a human action. The generation unit is configured to generate, based on the second time-series data, third time-series data notifying of the timing of the human action. The processing unit is configured to synchronize and output an output signal based on the first time-series data and an output signal based on the third time-series data, such that the timing of the human action for the first time-series data and the timing of the human action for the third time-series data match.

Signal processing device, signal processing system, and signal processing method

An image control system includes an estimation circuit, an obtaining circuit, and an image control circuit, which circuits can be at least one processor. The estimation circuit receives an output from a trained model in response to an input of an acoustic signal indicating a performance of a musical piece. The estimation circuit estimates musical performance information based on the output. The musical performance information is associated with the performance indicated by the acoustic signal relative to the musical piece. The obtaining circuit obtains manipulation information that is associated with a playback of an image. The image control circuit controls the playback of the image based on the musical performance information. The image control circuit controls the playback of the image based on the manipulation information upon obtaining the manipulation information.

Image control system and method for controlling image

A musical score processing method that is realized by a computer includes receiving a control data related to displaying of a musical piece from a music processor that executes a process related to the musical piece and that is realized by a music processing program, using inter-application communication, based on a musical score processing program, and executing a display control process by displaying a musical score of the musical piece on a display device and controlling the displaying in accordance with the control data that has been received, based on the musical score processing program. The music processing program is separate from the musical score processing program.

Musical score processing method and musical score processing system

A chord estimation apparatus estimates a first chord from an audio signal, and estimates a second chord by inputting the estimated first chord to a trained model that has learned a chord modification tendency.

Chord estimation method and chord estimation apparatus

Once obtained a turn-over instruction to proceed a score on one image area to which one grand staff among plural grand staves displayed on a screen of a user I/F belongs, a controller of a score displaying apparatus performs a process of scrolling the image areas upward on the screen of the user I/F, disposing the image area on which the turn-over instruction is accepted at a highest level of the screen of the user I/F, sequentially aligning a plurality of consecutive image areas in a forward direction on a timeline of the score with respect to the image area on which the turn-over instruction is accepted below the image area on which the turn-over instruction is accepted, and displaying the image areas on the screen of the user I/F.

Score displaying method and storage medium

A controller of a score displaying apparatus operates in a view mode displaying a score on a screen of a user I/F, and a writing mode obtaining information indicating writing on the score via the user I/F. In response to one operation specifying one grand staff by a user in the view mode, the controller performs a process of enlarging an image of an image area to which the specified grand staff belongs and displaying the enlarged image on a foreground of a center of the screen, and performs a process of switching a control mode from the view mode to the writing mode.

A user inputs, as a query pattern, a desired search-object rhythm pattern using a control, corresponding to a desired one of a plurality of performance parts constituting a performance data set (automatic accompaniment data set), in a rhythm input device. An input rhythm pattern storage section stores the input rhythm pattern (query pattern) into a RAM on the basis of a clock signal output from a bar line clock output section and input trigger data. A part identification section identifies a search-object performance part corresponding to the user-operated control. For the identified performance part, a rhythm pattern search section searches an automatic accompaniment database for an automatic accompaniment data set including a rhythm pattern that matches, i.e. has the highest similarity to, the input rhythm pattern (query pattern).

Performance data search using a query indicative of a tone generation pattern

A user inputs, as a query pattern, a desired search-object rhythm pattern by operating a rhythm input device. At that time, a determination is made of a style of operation performed by a user on a rhythm input device, such as whether the user has operated a single key or a plurality of keys, or duration or intensity of the operation, and then, a user-intended performance part is identified, on the basis of the determined style of operation, from among one or more performance parts constituting a performance data set (automatic accompaniment data set). For the thus-identified performance part, a rhythm pattern search section searches an automatic accompaniment database for an automatic accompaniment data set including a rhythm pattern that matches the input rhythm pattern (query pattern).

When a music audio to be analyzed is to be divided into a plurality of possible pattern segments based on estimated beat positions, it is divided in a plurality of different ways according to individual division models obtained by sequentially shifting the leading or first beat position of the possible pattern segments. Such division can provide plural sets of possible pattern segments with each of the sets corresponding to one of the division models. For each of the sets, comparison is made between individual possible pattern segments and individual reference performance patterns. For each of the possible pattern segments of the set, at least one reference performance pattern that matches the possible pattern segment is determined. Then, at least one combination of reference performance patterns is identified for each of the sets of possible pattern segments. One optimal combination is selected from among the identified combinations.

Technique for analyzing rhythm structure of music audio data

A music analysis apparatus has a feature extractor and an analysis processor. The feature extractor generates a time series of feature values from a sequence of notes which is designated as an object of analysis. The analysis processor computes an evaluation index value which indicates a probability that the designated sequence of notes is present in each of a plurality of reference music pieces by applying a probabilistic model to the time series of the feature values generated from the designated sequence of notes. The probabilistic model is generated by machine learning of the plurality of reference music pieces using time series of feature values obtained from the reference music pieces.

Music 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

Mask generation section generates an evaluating mask, indicative of a degree of dissonance with a target sound per each frequency along a frequency axis, by setting, for each of a plurality of peaks in spectra of the target sound, a dissonance function indicative of relationship between a frequency difference from the peak and a degree of dissonance with a component of the peak. Index calculation section collates spectra of an evaluated sound with the evaluating mask to thereby calculate a consonance index value indicative of a degree of consonance or dissonance between the target sound and the evaluated sound.

Sound evaluation device and method for evaluating a degree of consonance or dissonance between a plurality of sounds

A sound signal processing apparatus which is capable of correctly detecting expression modes and expression transitions of a song or performance from an input sound signal. A sound signal produced by performance or singing of musical tones is input and divided into frames of predetermined time periods. Characteristic parameters of the input sound signal are detected on a frame-by-frame basis. An expression determining process is carried out in which a plurality of expression modes of a performance or song are modeled as respective states, the probability that a section including a frame or a plurality of continuous frames lies in a specific state is calculated with respect to a predetermined observed section based on the characteristic parameters, and the optimum route of state transition in the predetermined observed section is determined based on the calculated probabilities so as to determine expression modes of the sound signal and lengths thereof.

Sound signal expression mode determining apparatus method and program

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.

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Hamamatsu, Japan

Takuya Fujishima