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A brief introduction to multichannel noise reduction with deep neural networks

Romain Serizel 1, 2
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Over the past decade deep learning has become the state-of-the-art in many applications including several tasks of speech and audio processing. It has recently been applied to multichannel speech enhancement, outperforming most of the classical approaches. In this presentation, I will present a short overview of some deep learning architectures that are currently used. I will then describe the problem of multichannel speech enhancement and a solution to this problem: the multichannel Wiener filters. Finally, I will present recent works that capitalize on both multichannel filters resulting from decades of work in signal processing and on the modeling power of deep learning to design deep learning based multichannel speech enhancement algorithms that are now the state-of-the-art in the domain.
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https://hal.inria.fr/hal-02506387
Contributor : Romain Serizel <>
Submitted on : Friday, June 5, 2020 - 8:39:11 AM
Last modification on : Friday, June 19, 2020 - 3:30:40 AM

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  • HAL Id : hal-02506387, version 1

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Romain Serizel. A brief introduction to multichannel noise reduction with deep neural networks. SpiN 2020 - 12th Speech in Noise Workshop, Jan 2020, Toulouse, France. ⟨hal-02506387⟩

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