Differentiable wavetable synthesizer using plurality of machine learning models to reduce computational complexity of audio synthesis

A method, comprising:

• obtaining at least one subset of a plurality of wavetables, wherein each of the plurality of wavetables comprises a waveform associated with a unique timbre, wherein the plurality of wavetables are generated by a first machine learning model based on input features, and wherein the input features comprise at least timbre embedding extracted from a dataset of sounds;
• initializing another machine learning model with the at least one subset of the plurality of wavetables, wherein the another machine learning model is configured to reduce a computational complexity of audio synthesis;
• and
• producing an audio item based on data output from the another machine learning model.

A system, comprising:

• at least one processor;
• and
• at least one memory communicatively coupled to the at least one processor and storing instructions that upon execution by the at least one processor cause the system to perform operations, the operations comprising:
• extracting features from a dataset of sounds, wherein the features comprise at least timbre embedding;
• input the features to a first machine learning model;
• generating a plurality of wavetables by the first machine learning model based on the features, wherein each of plurality of wavetables comprises a waveform associated with a unique timbre;
• initializing another machine learning model with at least one subset of the plurality of wavetables, wherein the another machine learning model is configured to reduce a computational complexity of audio synthesis;
• and
• generating an audio item based on data output from the another machine learning model.

A non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a processor cause the processor to implement operations, the operation comprising:

• extracting features from a dataset of sounds, wherein the features comprise at least timbre embedding;
• input the features to a first machine learning model;
• generating a plurality of wavetables by the first machine learning model based on the features, wherein each of plurality of wavetables comprises a waveform associated with a unique timbre;
• initializing another machine learning model with at least one subset of the plurality of wavetables, wherein the another machine learning model is configured to reduce a computational complexity of audio synthesis;
• and
• generating an audio item based on data output from the another machine learning model.