A Novel Normalized Cross-Correlation Based Echo-Path Change Detector

A double-talk detector is used to freeze acoustic echo canceller's (AEC) filter adaptation during periods of near-end speech. Increased sensitivity towards double-talk results in declaring echo-path changes as double-talk which adversely effects the performance of an AEC as we freeze adaptation when we really need to adapt. Thus, we need an efficient and simple echo-path change detector so as to differentiate any echo-path variations from double-talk condition. In this paper, we derive a novel test statistic for echo-path change detection. The proposed decision statistic detects any echo-path variations, is normalized properly and is computationally very efficient as compared to existing techniques. Simulations demonstrate the efficiency of the proposed algorithm.