Sound generation method using machine learning model, training method for machine learning model, sound generation device, training device, non-transitory computer-readable medium storing sound generation program, and non-transitory computer-readable medium storing training program

A training method realized by a computer, the training method comprising:

• extracting, from reference data representing a sound waveform, a reference sound data sequence in which a musical feature amount changes over time at a prescribed fineness and an output feature amount sequence which is a time series of the musical feature amount;
• generating, from the output feature amount sequence, an input feature amount sequence in which the musical feature amount changes over time at a lower fineness than the prescribed fineness;
• and
• constructing a trained model that has learned an input-output relationship between the input feature amount sequence and the reference sound data sequence by machine learning that uses the input feature amount sequence and the reference sound data sequence.

A training device comprising:

• at least one processor configured to
• extract, from reference data representing a sound waveform, a reference sound data sequence in which a musical feature amount changes over time at a prescribed fineness and an output feature amount sequence which is a time series of the musical feature amount,
• generate, from the output feature amount sequence, an input feature amount sequence in which the musical feature amount changes over time at a lower fineness than the prescribed fineness, and
• construct a trained model that has learned an input-output relationship between the input feature amount sequence and the reference sound data sequence by machine learning that uses the input feature amount sequence and the reference sound data sequence.

A non-transitory computer readable medium storing a training program that causes one or a plurality of computers to perform operations comprising:

• extracting, from reference data representing a sound waveform, a reference sound data sequence in which a musical feature amount changes over time at a prescribed fineness and an output feature amount sequence that is a time series of the musical feature amount;
• generating, from the output feature amount sequence, an input feature amount sequence in which the musical feature amount changes over time at a lower fineness than the prescribed fineness;
• and
• constructing a trained model that has learned an input-output relationship between the input feature amount sequence and the reference sound data sequence by machine learning that uses the input feature amount sequence and the reference sound data sequence.