Performance Study of a Convolutional Time-Domain Audio Separation Network for Real-Time Speech Denoising

By Samuel Sonning, Christian Schüldt, Hakan Erdogan, Scott Wisdom. 2020

26 citations recorded by the source corpus.

Time-domain audio separation networks based on dilated temporal convolutions have recently been shown to perform very well compared to methods that are based on a time-frequency representation in speech separation tasks, even outperforming an oracle binary time-frequency mask of the speakers. This paper investigates the performance of such a time-domain network (Conv-TasNet) for speech denoising in a real-time setting, comparing various parameter settings. Most importantly, different amounts of lookahead are evaluated and compared to the baseline of a fully causal model. We show that a large part of the increase in performance between a causal and non-causal model is achieved with a lookahead of only 20 milliseconds, demonstrating the usefulness of even small lookaheads for many real-time applications.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Generative Speech Coding with Predictive Variance Regularization (2021, methodology): “The pruning makes the codec with TasNet run reliably on a Pixel 2 phone in single-threaded mode.”