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Joint NN-Supported 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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Submitted on : Monday, July 27, 2020 - 10:58:21 AM
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Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise. IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.3008974⟩. ⟨hal-02372579v4⟩



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