Sparseness-controlled Adaptive Algorithms for Echo Cancellation

In hands-free telephony, the acoustic coupling between the loudspeaker and the microphone generates echoes that can seriously degrade user experience. For this reason, effective Acoustic echo cancellation (AEC) is important to maintaining and improving the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the Acoustic impulse response s (AIRs) using adaptive algorithms. The performances of a range of algorithms, including Normalized least-mean-square (NLMS), Proportionate normalized least-mean-square (PNLMS), μ-law proportionate normalized least-mean-square (MPNLMS) and Improved proportionate normalized least-mean-square (IPNLMS), are studied in the context of both AEC and Network echo cancellation (NEC). This analysis presents insights into their tracking performances under both time-invariant and timevarying system conditions. In the context of AEC, it is shown that the level of sparseness in AIRs 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 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 sparsenesscontrolled approach. Simulation results, using White Gaussian noise (WGN) and speech input signals, show improved performance over existing methods. The proposed algorithms achieve these improvements with only a modest increase in computational complexity.