improving partition-block-based acoustic echo canceler in under-modeling scenarios

TL;DR

This paper analyzes a partitioned-block frequency-domain Kalman filter for acoustic echo cancellation, identifies bias issues in under-modeling scenarios, and proposes a modification to ensure optimal steady-state performance.

Contribution

The paper provides an analysis of the steady-state bias in PFKF and introduces a modification that guarantees optimal steady-state behavior in under-modeling scenarios.

Findings

The original PFKF suffers from biased steady-state solutions under model deficiency.

The proposed modification improves steady-state accuracy and convergence.

Simulations confirm enhanced performance of the modified PFKF.

Abstract

Recently, a partitioned-block-based frequency-domain Kalman filter (PFKF) has been proposed for acoustic echo cancellation. Compared with the normal frequency-domain Kalman filter, the PFKF utilizes the partitioned-block structure, resulting in both fast convergence and low time-latency. We present an analysis of the steady-state behavior of the PFKF and found that it suffers from a biased steady-state solution when the filter is of deficient length. Accordingly, we propose an effective modification that has the benefit of the guaranteed optimal steady-state behavior. Simulations are conducted to validate the improved performance of the proposed method.