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Journal Articles IEEE/ACM Transactions on Audio, Speech and Language Processing Year : 2018

Watermark-Driven Acoustic Echo Cancellation

Abstract

The performance of adaptive acoustic echo cancelers (AEC) is sensitive to the non-stationarity and correlation of speech signals. In this article, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the far-end speech. In the signal, we embed-in an extended conception of data hiding-an imperceptible white and stationary signal, i.e. a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely, and takes advantage of its appropriate properties (stationary and white) to improve the robustness of the two-stage AEC to the non-stationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as A-WdAEC (Adaptive Watermark driven AEC), the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as MLS-WdAEC, the watermark is built from maximum length sequences (MLS). Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/pre-processed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and under-modeling. Simulation results show the relevance of the "watermark-driven AEC" approach, compared to the classical "error driven AEC".
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Dates and versions

hal-01828978 , version 1 (13-07-2018)

Identifiers

Cite

Sonia Djaziri-Larbi, Gael Mahé, Imen Mezghani, Monia Turki, Mériem Jaidane. Watermark-Driven Acoustic Echo Cancellation. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2018, 26 (2), pp.367 - 378. ⟨10.1109/TASLP.2017.2778150⟩. ⟨hal-01828978⟩
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