Andy W. H. Khong

A Class of Sparseness-Controlled Algorithms for Echo Cancellation

In the context of acoustic echo cancellation (AEC), it is shown that the level of sparseness in acoustic impulse responses can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for network echo cancellation (NEC), we propose a class of AEC algorithms that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. Simulation results, using white Gaussian noise (WGN) and speech input signals, show improved performance over existing methods. The proposed algorithms achieve these improvement with only a modest increase in computational complexity.

Efficient Use Of Sparse Adaptive Filters

We present a novel adaptive algorithm exploiting the sparseness of an impulse response for network echo cancellation. This sparseness-controlled improved proportionate normalized least mean square (SC-IPNLMS) algorithm is based on IPNLMS which allocates a step-size gain proportional to each filter coefficient. The proposed SC-IPNLMS algorithm achieves improved convergence over IPNLMS by estimating the sparseness of the impulse response and allocating gains for each step- size such that a higher weighting is given to the proportionate term of the IPNLMS for sparse impulse responses. For a less sparse impulse response, a higher weighting will be allocated to the NLMS term. Simulation results presented show improved performance over the IPNLMS algorithm during convergence before and after an echo path change has been introduced. We also discuss the computational complexity of the proposed algorithm.

Selective-Tap Adaptive Filtering With Performance Analysis for Identification of Time-Varying Systems

Selective-tap algorithms employing the MMax tap selection criterion were originally proposed for low-complexity adaptive filtering. The concept has recently been extended to multichannel adaptive filtering and applied to stereophonic acoustic echo cancellation. This paper first briefly reviews least mean square versions of MMax selective-tap adaptive filtering and then introduces new recursive least squares and affine projection MMax algorithms. We subsequently formulate an analysis of the MMax algorithms for time-varying system identification by modeling the unknown system using a modified Markov process. Analytical results are derived for the tracking performance of MMax selective tap algorithms for normalized least mean square, recursive least squares, and affine projection algorithms. Simulation results are shown to verify the analysis.

Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms

Stereophonic acoustic echo cancellation has generated much interest in recent years due to the nonuniqueness and misalignment problems that are caused by the strong interchannel signal coherence. In this paper, we introduce a novel adaptive filtering approach to reduce interchannel coherence which is based on a selective-tap updating procedure. This tap-selection technique is then applied to the normalized least-mean-square, affine projection and recursive least squares algorithms for stereophonic acoustic echo cancellation. Simulation results for the proposed algorithms have shown a significant improvement in convergence rate compared with existing techniques.

A low delay and fast converging improved proportionate algorithm for sparse system identification

A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS) algorithm and the efficient implementation of the multidelay adaptive filtering (MDF) algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.

Proportionate Frequency Domain Adaptive Algorithms for Blind Channel Identification

We present fast-converging adaptive blind channel identification algorithms for acoustic room impulse responses. These new algorithms exploit the fast-convergence of the improved proportionate normalized least-mean-square (IPNLMS) algorithm and address the problem of delay inherent in frequency domain algorithms by employing the multi-delay filter (MDF) structure. Simulation results for both speech and white Gaussian noise show that the proposed algorithms outperform current frequency domain blind channel estimation algorithms

Affine projection and recursive least squares adaptive filters employing partial updates

We present order K affine projection and recursive least squares adaptive filters employing partial update schemes. The starting point of the work is the MMax tap-selection criterion in which, given a filter length L, only M coefficients are updated that correspond to the M largest magnitude elements of the regression vector. We extend this approach from its existing form of MMax-NLMS to new affine projection and recursive least squares schemes with supporting analysis and simulation results. We discuss the computational complexity of these approaches for two alternative sort procedures. Finally, we extend the MMax criterion to a multichannel case by introducing an exclusivity constraint and show the effectiveness of the resulting XM tap-selection criterion for application to stereophonic acoustic echo cancellation.

Selective-tap adaptive algorithms in the solution of the nonuniqueness problem for stereophonic acoustic echo cancellation

We investigate stereophonic acoustic echo cancellation in which solutions for the system can be nonunique and propose the use of selective-tap adaptive filters to address this problem. The main concept is to employ tap selection to optimize jointly for minimum interchannel coherence and maximum L/sub 2/-norm of the subselected tap-input vectors. The exclusive maximum (XM) tap-selection approach is proposed and applied to normalized least-mean squares (NLMS) and recursive least-squares (RLS) algorithm. We propose an approach for solving the nonuniqueness problem employing XM tap selection in combination with a nonlinear preprocessor. Simulation results show a significant improvement in convergence rate compared with existing techniques.

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

The performance in terms of misalignment of adaptive algorithms, in general, is dependent on the conditioning of the input signal covariance matrix. The performance of two-channel adaptive algorithms is further degraded by the high interchannel coherence between the two input signals. In this letter, we establish the relationship between interchannel coherence of the two input signals and condition of the corresponding covariance matrix for stereo acoustic echo cancellation application. We show how this relationship affects the misalignment of a frequency-domain adaptive algorithm. We provide simulation results for both white Gaussian noise and speech input to verify our mathematical analysis.

A Forced Spectral Diversity Algorithm for Speech Dereverberation in the Presence of Near-Common Zeros

Blind identification of single-input multiple-output (SIMO) systems is not normally possible if common zeros exist in the channels. Studies of measured acoustic SIMO systems show that near-common zeros occur in such systems as encountered in the speech dereverberation task. We therefore introduce a method to add additional diversity to the SIMO system to be identified which we term forced spectral diversity (FSD) and we show that its use leads to an identification-equalization approach that gives improved dereverberation. As part of this work, we show the link between channel diversity and the effect of common zeros. We also define and discuss in more detail the concept and impact of near-common zeros. The proposed algorithm is presented specifically for a two-channel system where such near-common zeros exist.