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Efficient Trainable Front-Ends for Neural Speech Enhancement

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arxiv 2002.09286 v1 pith:BENUCBQX submitted 2020-02-20 eess.AS cs.LGcs.NEcs.SDstat.ML

Jonah Casebeer , Umut Isik , Shrikant Venkataramani , Arvindh Krishnaswamy This is my paper · ORCID
classification eess.AScs.LGcs.NEcs.SDstat.ML
keywords trainableenhancementfourierneuralspeechtransformefficientfront-ends
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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.

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