Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

Guillaume Carbajal 1 Romain Serizel 1 Emmanuel Vincent 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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [48 references]  Display  Hide  Download
Contributor : Guillaume Carbajal <>
Submitted on : Wednesday, November 20, 2019 - 2:49:31 PM
Last modification on : Tuesday, December 17, 2019 - 2:25:23 AM


Files produced by the author(s)


  • HAL Id : hal-02372579, version 1


Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. Joint DNN-Based Multichannel Reduction of Acoustic Echo, Reverberation and Noise. 2019. ⟨hal-02372579v1⟩



Record views


Files downloads