Multi-channel Time-Varying Covariance Matrix Model for Late Reverberation Reduction

TL;DR

This paper introduces a multi-channel, time-varying covariance matrix model for reducing late reverberation in speech signals, demonstrating robustness against ATF fluctuations and improved performance with an extended input signal approach.

Contribution

It proposes a novel covariance matrix model that accounts for time-varying reverberation and past microphone signals, enhancing reverberation reduction robustness.

Findings

Effective reverberation reduction in time-varying ATF scenarios

The extended microphone input model outperforms the original model

Robustness against fluctuations of the acoustic transfer function

Abstract

In this paper, a multi-channel time-varying covariance matrix model for late reverberation reduction is proposed. Reflecting that variance of the late reverberation is time-varying and it depends on past speech source variance, the proposed model is defined as convolution of a speech source variance with a multi-channel time-invariant covariance matrix of late reverberation. The multi-channel time-invariant covariance matrix can be interpreted as a covariance matrix of a multi-channel acoustic transfer function (ATF). An advantageous point of the covariance matrix model against a deterministic ATF model is that the covariance matrix model is robust against fluctuation of the ATF. We propose two covariance matrix models. The first model is a covariance matrix model of late reverberation in the original microphone input signal. The second one is a covariance matrix model of late reverberation in an extended microphone input signal which includes not only current microphone input signal but also past microphone input signal. The second one considers correlation between the current microphone input signal and the past microphone input signal. Experimental results show that the proposed method effectively reduces reverberation especially in a time-varying ATF scenario and the second model is shown to be more effective than the first model.