Acoustic echo cancellation using NLMS-neural network structures
作者： A.N. Birkett , R.A. Goubran
DOI: 10.1109/ICASSP.1995.479485
关键词:
摘要: One of the limitations linear adaptive echo cancellers is nonlinearities which are generated mainly in loudspeaker. The complete acoustic channel can be modelled as a nonlinear system convolved with dispersive channel. Two new canceller models developed to improve performance. first model consists time-delay feedforward neural network (TDNN) and second memoryless followed by an normalized least mean square (NLMS) structure. Simulations demonstrate that both based structures return loss enhancement (ERLE) performance compared NLMS canceller. Experimental results using TDNN improved ERLE 10 dB at low medium loudspeaker volumes.
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