Performance analysis method
AssigneeYamaha Corporation
InventorAkira Maezawa
Life of this patent
Estimating the term…
Abstract
A performance analysis method according to the present invention includes generating information related to a performance tendency of a user, from observed performance information relating to a performance of a musical piece by the user and inferred performance information that occurs when the musical piece is performed based on a specific tendency.
Claims5
A performance analysis method for musical piece information containing a plurality of pieces of unit information representing contents of a musical piece, the method comprising:
- generating a plurality of pieces of inferred performance information corresponding to a plurality of different candidate tendencies, for each unit period of a plurality of unit periods in the musical piece information, from the musical piece information using first and second neural networks by, for each of the plurality of different candidate tendencies:
- generating, with the first neural network, feature information based on pieces of unit information, from among the plurality of pieces of unit information, included in a predetermined analysis period, which includes a current unit period;
- generating, with the second neural network, a linear predictor coefficient based on the generated feature information and past pieces of inferred performance information from a previous unit period;
- and
- calculating the inferred performance information from the generated linear predictor coefficient and the past pieces of inferred performance information;
- generating, for each unit period, observed performance information from a time series of plural pieces of instruction information output by a performance apparatus, the observed performance information being a variable relating to a tendency of a performance of the musical piece;
- and
- generating, for each unit period, performance tendency information related to a performance tendency of a user from the generated plurality of pieces of inferred performance information and the generated observed performance information by comparing each of the plurality of pieces of inferred performance information respectively generated for the plurality of different candidate tendencies with the generated observed performance information.
A performance analysis apparatus comprising:
- a storage unit storing musical piece information containing a plurality of pieces of unit information representing contents of a musical piece;
- and
- a control unit including at least one processor, configured to:
- generate a plurality of pieces of inferred performance information corresponding to a plurality of different candidate tendencies, for each unit period of a plurality of unit periods in the musical piece information, from the musical piece information using first and second neural networks by, for each of the plurality of different candidate tendencies by, and for each of the plurality of different candidate tendencies that:
- generates, with the first neural network, feature information based on pieces of unit information, from among the plurality of pieces of unit information, included in a predetermined analysis period, which includes a current unit period;
- generates, with the second neural network, a linear predictor coefficient based on the generated feature information and past pieces of inferred performance information from a previous unit period;
- and
- calculates the inferred performance information from the generated linear predictor coefficient and the past pieces of inferred performance information;
- generate, for each unit period, observed performance information from a time series of plural pieces of instruction information output by a performance apparatus, the observed performance information being a variable relating to a tendency of a performance of the musical piece;
- and
- generate, for each unit period, performance tendency information related to a performance tendency of a user from the generated plurality of pieces of inferred performance information and the generated observed performance information by comparing each of the plurality of pieces of inferred performance information respectively generated for the plurality of different candidate tendencies with the generated observed performance information.
A non-transitory storage medium storing a program executable by a computer to execute a performance analysis method for musical piece information containing a plurality of pieces of unit information representing contents of a musical piece, the method comprising:
- generating a plurality of pieces of inferred performance information corresponding to a plurality of different candidate tendencies, for each unit period of a plurality of unit periods in the musical piece information, from the musical piece information using first and second neural networks by, for each of the plurality of different candidate tendencies:
- generating, with the first neural network, feature information based on pieces of unit information, from among the plurality of pieces of unit information, included in a predetermined analysis period, which includes a current unit period;
- generating, with the second neural network, a linear predictor coefficient based on the generated feature information and past pieces of inferred performance information from a previous unit period;
- and
- calculating the inferred performance information from the generated linear predictor coefficient and the past pieces of inferred performance information;
- and
- generating, for each unit period, observed performance information from a time series of plural pieces of instruction information output by a performance apparatus, the observed performance information being a variable relating to a tendency of a performance of the musical piece;
- generating, for each unit period, performance tendency information related to a performance tendency of a user from the generated plurality of pieces of inferred performance information and the generated observed performance information by comparing each of the plurality of pieces of inferred performance information respectively generated for the plurality of different candidate tendencies with the generated observed performance information.
Dependencies are inferred from the claim text.