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Efficient Trainable Front-Ends for Neural Speech Enhancement

2020-02-20 01:51:15
Jonah Casebeer, Umut Isik, Shrikant Venkataramani, Arvindh Krishnaswamy
arXiv_SD
     
Abstract

Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) front-ends trainable. In current literature, these are implemented as large Discrete Fourier Transform matrices; which are prohibitively inefficient for low-compute systems. We present an efficient, trainable front-end based on the butterfly mechanism to compute the Fast Fourier Transform, and show its accuracy and efficiency benefits for low-compute neural speech enhancement models. We also explore the effects of making the STFT window trainable.

Abstract (translated)

URL

https://arxiv.org/abs/2002.09286

PDF

https://arxiv.org/pdf/2002.09286.pdf

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