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

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United States of America

PATENT NO 12198673
APP PUB NO 20230154451A1
SERIAL NO

17525814

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The present disclosure describes techniques for differentiable wavetable synthesizer. The techniques comprise extracting features from a dataset of sounds, wherein the features comprise at least timbre embedding; input the features to the first machine learning model, wherein the first machine learning model is configured to extract a set of N×L learnable parameters, N represents a number of wavetables, and L represents a wavetable length; outputting a plurality of wavetables, wherein each of plurality of wavetables comprises a waveform associated with a unique timbre, the plurality of wavetables form a dictionary, and the plurality of wavetables are portable to perform audio-related tasks. Finally, the said wavetables are used to initialize another machine learning model so as to help reduce computational complexity of an audio synthesis obtained as output of the another machine learning model.

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LEMON INCP 0 BOX 31119 GRAND PAVILION HIBISCUS WAY 802 WEST BAY ROAD GRAND CAYMAN KYL-1205

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Avent, Matthew David London, GB 3 27
Chen, Jitong Los Angeles, US 14 75
Hantrakul, Lamtharn Singapore, SG 5 4
Shan, Siyuan Los Angeles, US 1 0
Trevelyan, David London, GB 5 2

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