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Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
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.
Cited literature [53 references]
https://hal.inria.fr/hal-02372579
Contributor : Guillaume Carbajal Connect in order to contact the contributor
Submitted on : Monday, July 27, 2020 - 10:58:21 AM
Last modification on : Wednesday, November 3, 2021 - 7:56:47 AM
Contributor : Guillaume Carbajal Connect in order to contact the contributor
Submitted on : Monday, July 27, 2020 - 10:58:21 AM
Last modification on : Wednesday, November 3, 2021 - 7:56:47 AM
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