Stereophonic acoustic echo cancellation: Analysis of the misalignment in the frequency domain

Published versio

An Improved PNLMS Algorithm

The proportionate normalized least mean square (PNLMS) algorithm was developed for use in network echo cancelers. In comparison to the normalized least mean square (NLMS) algorithm, PNLMS has a very fast initial convergence and tracking when the echo path is sparse. Unfortunately, when the impulse response is dispersive, the PNLMS converges much slower than NLMS. This implies that the rule proposed in PNLMS is far from optimal. In many simulations, it seems that we fully benefit from PNLMS only when the impulse response is close to a delta function. We propose a new rule that is more reliable than the one used in PNLMS. Many simulations show that the new algorithm (improved PNLMS) performs better than NLMS and PNLMS, whatever the nature of the impulse respons

A Speech Distortion and Interference Rejection Constraint Beamformer

Signals captured by a set of microphones in a speech communication system are mixtures of desired and undesired signals and ambient noise. Existing beamformers can be divided into those that preserve or distort the desired signal. Beamformers that preserve the desired signal are, for example, the linearly constrained minimum variance (LCMV) beamformer that is supposed, ideally, to reject the undesired signal and reduce the ambient noise power, and the minimum variance distortionless response (MVDR) beamformer that reduces the interference-plus-noise power. The multichannel Wiener filter, on the other hand, reduces the interference-plus-noise power without preserving the desired signal. In this paper, a speech distortion and interference rejection constraint (SDIRC) beamformer is derived that minimizes the ambient noise power subject to specific constraints that allow a tradeoff between speech distortion and interference-plus-noise reduction on the one hand, and undesire d signal and ambient noise reductions on the other hand. Closed-form expressions for the performance measures of the SDIRC beamformer are derived and the relations to the aforementioned beamformers are derived. The performance evaluation demonstrates the tradeoffs that can be made using the SDIRC beamformer

A Practical Data-Reuse Adaptive Algorithm for Acoustic Echo Cancellation

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Double-Talk Robust Fast Converging Algorithms for Network Echo Cancellation

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