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.

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk
• Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization

Cited by in corpus (7)

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• A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
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• Improved Lite Audio-Visual Speech Enhancement
• Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach
• Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices
• Joint Online Multichannel Acoustic Echo Cancellation, Speech Dereverberation and Source Separation