3 research outputs found
Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
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