RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

TL;DR

This paper introduces an RLS-based adaptive dereverberation method with a novel time-varying forgetting factor to effectively track abrupt changes in the target speaker's position, improving convergence speed and steady-state performance.

Contribution

It proposes a new RLS-based adaptive dereverberation algorithm with a time-varying forgetting factor for better tracking of abrupt speaker position changes.

Findings

Effective tracking of abrupt speaker position changes demonstrated in simulations.

Improved convergence speed and steady-state performance over traditional methods.

Enhanced dereverberation quality in dynamic speaker scenarios.

Abstract

Adaptive algorithm based on multi-channel linear prediction is an effective dereverberation method balancing well between the attenuation of the long-term reverberation and the dereverberated speech quality. However, the abrupt change of the speech source position, usually caused by the shift of the speakers, forms an obstacle to the adaptive algorithm and makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this paper, the RLS-based adaptive multi-channel linear prediction method is investigated and a time-varying forgetting factor based on the relative weighted change of the adaptive filter coefficients is proposed to effectively tracing the abrupt change of the target speaker position. The advantages of the proposed scheme are demonstrated in the simulations and experiments.