Adaptive system identification and equalization algorithms for acoustic echo cancellation and speech dereverberation
Date of Issue2013
School of Electrical and Electronic Engineering
A class of adaptive algorithms for acoustic echo cancellation (AEC) and speech dereverberation have been developed and analyzed in this thesis. The starting point of this work is the affine projection (AP) based non-blind channel identification algorithms for AEC. The proposed. sparseness constrained improved proportionate AP algorithms (SC-IPAPA-I and SC-IPAPA-II) exploit the sparseness of the estimated channel and allocate different effective step sizes accordingly. The performance of these algorithms has been studied in the context of single-channel AEC and tracking capability. The performance of a blind channel identification algorithm with additive noise is studied by providing reasons for the degradation in perforrnance of the rnultichannel least-rnean-square (MCLMS) algorithm in the presence of additive noise. Subsequently, it is shown mathematically, through a cross-correlation based cost function, that minimizing the power of a filtered version of the received signals can suppress the noise effect. The performance of MCLMS and improved lVICLlVIS (LVICLMS) are evaluated using the normalized projection rnisalignment via lVlonte Carlo simulations. It is shown through simulations that the well-known normalized rnultichannel frequency-domain least-mean-square (NMCFLMS) algorithrn, originally developed for acoustic impulse response (AIR) identification in the frequency domain, also suffers from the noise robustness problem. A noise robust blind AIR estima.tion algorithm is then proposed. Inspired by the trivial solution achieved by NMCFLJVIS in the presence of noise, the proposed direct-path NMCFLMS algorithm with power constraint (DP-NMCFLMS-PC) jointly applies direct-path and power constraint to improve its robustness against noise. The DP-NMCLFMS-PC not only addresses the noise robustness issue but also achieves fast convergence compared to NMCFLMS. The adaptive multiple input/output inversion theorem (A-MINT) algorithm has been developed for channel equalization with application to speech dereverberation. In order to increase its convergence rate, the proposed algorithm suppresses any undesired non-zero coefficients in the estimated Kronecker delta function iteratively. This is achieved by applying the sparseness measure of the estimated Kronecker delta function and using it as an additional constraint to A-MINT. Unlike existing channel equalization systems, the proposed auto-relation aided MINT (A-RAM) algorithrn, which achieves good equalization performance, takes into account how the received signals are generated during its adaptation process. The differential output signals from two sub-systems are then utilized in the cost function during equalization. Sirnulation results have shown that the proposed A-RAM algorithm can achieve a higher rate of convergence leading to better dereverberated speech signal cornpared to existing MINT-based equalization algorithms.
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing