The Combination of Several Decorrelation Methods to Improve Acoustic Feedback Cancellation

TL;DR

This paper enhances acoustic feedback cancellation by integrating multiple decorrelation techniques into a frequency-domain Kalman filter system, demonstrating that their combined use significantly improves performance on standard datasets.

Contribution

It introduces a novel combination of decorrelation methods for feedback cancellation, showing that their integration outperforms individual approaches.

Findings

Combined extensions improve cancellation performance

All proposed methods contribute positively individually

System achieves better metrics on public datasets

Abstract

This paper extends an acoustic feedback cancellation system by incorporating multiple decorrelation methods. The baseline system is based on a frequency-domain Kalman filter implemented in a multi-delay structure. The proposed extensions include a variable time delay line, prediction, distortion compensation, and a simplified reverberation model. Each extension is analyzed, and a practical parameter range is defined. While existing literature often focuses on a single extension, such as prediction, to describe an optimal system, this work demonstrates that each individual extension contributes to performance improvements. Furthermore, the combination of all proposed extensions results in a superior system. The evaluation is conducted using publicly available datasets, with performance assessed through system distance metrics and the objective speech quality measure PSEQ.