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Joint DNN-Based Multichannel Reduction of Acoustic Echo, Reverberation and Noise

Guillaume Carbajal 1 Romain Serizel 1 Emmanuel Vincent 1 Eric Humbert 2
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. We propose to model the target and residual signals after linear echo cancellation and dereverberation using a multichannel Gaussian modeling framework and to jointly represent their spectra by means of a neural network. We develop an iterative block-coordinate ascent algorithm to update all the filters. We evaluate our system on real recordings of acoustic echo, reverberation and noise acquired with a smart speaker in various situations. The proposed approach outperforms in terms of overall distortion a cascade of the individual approaches and a joint reduction approach which does not rely on a spectral model of the target and residual signals.
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Contributor : Guillaume Carbajal <>
Submitted on : Thursday, December 12, 2019 - 9:43:49 AM
Last modification on : Wednesday, May 20, 2020 - 1:48:21 AM


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  • HAL Id : hal-02372579, version 2


Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. Joint DNN-Based Multichannel Reduction of Acoustic Echo, Reverberation and Noise. 2019. ⟨hal-02372579v2⟩



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