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arXiv · 2111.10003

Differentiable Wavetable Synthesis

Siyuan Shan·Lamtharn Hantrakul·Jitong Chen·Matt Avent·David Trevelyan
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Abstract

Differentiable Wavetable Synthesis (DWTS) is a technique for neural audio synthesis which learns a dictionary of one-period waveforms i.e. wavetables, through end-to-end training. We achieve high-fidelity audio synthesis with as little as 10 to 20 wavetables and demonstrate how a data-driven dictionary of waveforms opens up unprecedented one-shot learning paradigms on short audio clips. Notably, we show audio manipulations, such as high quality pitch-shifting, using only a few seconds of input audio. Lastly, we investigate performance gains from using learned wavetables for realtime and interactive audio synthesis.

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Siyuan Shan, Lamtharn Hantrakul, Jitong Chen, Matt Avent, David Trevelyan. 2022-02-13. Differentiable Wavetable Synthesis. https://arxiv.org/abs/2111.10003

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