Enhancement of Residual Echo for Robust Acoustic Echo Cancellation

Enhancement of Residual Echo for Robust Acoustic Echo Cancellation,10.1109/TASL.2011.2159592,IEEE Transactions on Audio, Speech & Language Processing,

Enhancement of Residual Echo for Robust Acoustic Echo Cancellation  
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This paper examines the technique of using a noise- suppressing nonlinearity in the adaptive filter error feedback-loop of an acoustic echo canceler (AEC) based on the least mean square (LMS) algorithm when there is an interference at the near end. The source of distortion may be linear, such as local speech or background noise, or nonlinear due to speech coding used in the telecommunication networks. Detailed derivation of the error re- covery nonlinearity (ERN), which "enhances" the filter estimation errorprior to the adaptation inorder to assist the linearadaptation process, will be provided. Connections to other existing AEC and signal enhancement techniques will be revealed. In particular, the error enhancement technique is well-founded in the information- theoretic sense and has strong ties to independent component anal- ysis (ICA),which is the basisfor blind source separation (BSS) that permits unsupervised adaptation in the presence of multiple inter- fering signals. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. The system ap- proach to robust AEC will be motivated, where a proper integra- tion of the LMS algorithm with the ERN into the AEC "system" allows for continuous and stable adaptation even during double talk without precise estimation of the signal statistics. The error enhancement paradigm encompasses many traditional signal en- hancement techniques and opens up an entirely new avenue for solving the AEC problem in a real-world setting. Index Terms—Acoustic echo cancellation (AEC), error enhance- ment, error nonlinearity, independent component analysis (ICA), robust statistics, semi-blind source separation (SBSS), system ap- proach to signal enhancement.
Journal: IEEE Transactions on Audio, Speech & Language Processing - TASLP , vol. 20, no. 1, pp. 163-177, 2012
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