Nonlinear Acoustic Echo Cancellation

Abstract

As voice communication becomes an ever-more important and pervasive part of our everyday lives, the issue of speech quality becomes more critical. One of the reasons for the undesirable quality degradation is the appearance of audible echoes. This kind of quality degradation is inherently from network equipment and end-user devices. To increase speech quality and improve listening experience, it is necessary to design effective acoustic echo cancellation systems. Echo cancellation has been studied for several decades, and today it is easy to implement echo cancellers on digital signal processors (DSPs). However, certain difficulties still remain to meet the requirements imposed by the echo cancellation standard, and some fundamental challenges still wait for breakthroughs. One of them is the nonlinearity in the acoustic echo path. Nonlinearity usually comes from the price competition in the market of consumer electronics. For economic purposes, the small-sized and low-cost analog components that exhibit nonlinearity, such as loudspeakers and power amplifiers (PAs), are utilized. An echo canceller performs poorly or does not work at all in the system where the net nonlinear distortion is higher than a certain value. In this dissertation, we address the aforementioned nonlinearity issue in acoustic echo cancellation systems. To sufficiently remove the nonlinear acoustic echo, nonlinear adap- tive filters have been proposed in the literature to identify the nonlinear acoustic echo path. The identification is done by minimizing the mean square error (MSE) between the microphone-received signal and estimated echo signal. In this way, the echo signal can be reconstructed and subtracted from the microphone-received signal. However, the issues of stability, convergence rate, and computational complexity inhibit nonlinear acoustic echo cancellers (NAECs) from practical implementation. Thus, we are motivated to design effi- cient NAECs in terms of stability, fast convergence rate, and low computational complexity. First, we propose to perform nonlinearity identification based on the coherence function, which guarantees the stability of the nonlinear adaptive system. Later on, we present a general framework for echo cancellation systems using a shortening filter that entails low computational burden and fast convergence rate. Moreover, we develop methods to re- move the system nonlinearity based on the coherence function, including the predistortion linearization, nonlinear residual echo suppressor, and Hammerstein-Wiener model-based NAEC.