Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A controller of a score displaying apparatus operates in a view mode displaying a score on a screen of a user I/F 12, and a writing mode obtaining information indicating writing on the score via the user I/F 12. 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.

Abstract: A sound signal processing apparatus and system includes a control device having a processor and a memory storing instructions that causes the processor to acquire a plurality of sound signals representing sounds recorded at a plurality of positions in a sound space, respectively, and correlate the plurality of sound signals with a plurality of pieces of position information indicating the recording positions, respectively. Sound signals, from among the acquired plurality of signals correlated with the plurality of pieces of position information, are selected as a mixing target. The selected sound signals are mixed to be synchronized with each other.

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: A controller of a score displaying apparatus operates in a view mode displaying a score on a screen of a user I/F 12, and a writing mode obtaining information indicating writing on the score via the user I/F 12. 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).