Real Time Speech Enhancement in the Waveform Domain

October 25, 2020

Abstract

We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working directly on the raw waveform.

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AUTHORS

Written by

Alexandre Defossez

Gabriel Synnaeve

Yossef (Yossi) Adi

Publisher

InterSpeech

Research Topics

Speech & Audio

Natural Language Processing (NLP)

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