Echo cancelers which cover longer impulse responses (greater than or equal to 64 ms) are desirable. Long responses create a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers devised by the ITU (International Telecommunication Union). In general, faster convergence implies a higher sensitivity to near-end disturbances, especially "double-talk". Recently, a fast converging algorithm called proportionate NLMS (normalized least mean squares) algorithm (PNLMS) has been proposed. This algorithm exploits the sparseness of the echo path. In this paper we propose a method for making the PNLMS algorithm more robust against double-talk. The slower divergence rate of these robust algorithms in combination with a standard Geigel double-talk detector improves the performance of a network echo canceler considerably during double-talk. This results in the robust PNLMS algorithm which diverges much slower than PNLMS and standard NLMS. A generalization of the robust PNLMS algorithm to a robust proportionate affine projection algorithm (APA) is also presented. It converges very fast, and unlike PNLMS, is not as dependent on the assumption of a sparse echo path response. Trade off between convergence and divergence rate is easily tuned with one parameter and the added complexity is about 7 instructions per sample.
T. Gansler et al., "Double-Talk Robust Fast Converging Algorithms for Network Echo Cancellation," Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 1999, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/ASPAA.1999.810888
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 1999
Electrical and Computer Engineering
Keywords and Phrases
ITU; International Telecommunication Union; PNLMS Algorithm; Complexity; Computational Complexity; Convergence of Numerical Methods; Convergence Rate; Divergence Rate; Double-Talk Robust Fast Converging Algorithms; Echo Cancelers; Echo Suppression; Impulse Response; Least Mean Squares Methods; Near-End Disturbances; Network Echo Cancellation; Normalized Least Mean Squares; Proportionate NLMS Algorithm; Proportionate Affine Projection Algorithm; Signal Detection; Sparse Echo Path Response; Standard Geigel Double-Talk Detector; Standard NLMS; Telecommunication Networks; Transient Response
Article - Conference proceedings
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