PhD

dissertationMultichannel audio is necessary to enhance sound realism in a teleconferencing system. Sound spatialization to improve the quality of service can be achieved through the use of three-dimensional (3-D) audio systems. A 3-D audio system implemented using loudspeakers inherently suffers from the problem of acoustic crosstalk cancellation. There is also potential for acoustic signals from the multiple loudspeakers to be picked up by the microphones, thus resulting in echoes. While stereophonic acoustic echo cancellation (AEC) may be seen as a simple generalization of the well-known single-channel AEC, it is a fundamentally far more complex and challenging problem to solve. This work addresses two distinct but related problems of acoustic crosstalk cancellation and stereophonic acoustic echo cancellation. We propose methods for designing immersive audio rendering filters for a single listener using loudspeakers. The filters for crosstalk cancellation are assumed to have finite impulse responses and are designed using the minimax criterion. In addition to the traditional Atal- Schroeder crosstalk canceler structure, we will also explore an alternate topology that requires the approximation of a single filter. In general, the minimax approach provides improved low frequency performance leading to a better overall separation of the direct path and cross path transfer functions than the least-squares designs. The performance of the single filter structure is also shown to be better than that of the traditional crosstalk cancellation structure. We also present a new class of adaptive filtering algorithms to solve the stereophonic AEC problem in teleconferencing systems. In a stereophonic setup, there exists a strong cross-correlation between the two input audio channels that makes the problem difficult to solve. In the past, nonlinearities have been introduced to reduce this correlation. However, nonlinearities introduce additional harmonics that are undesirable. We propose an elegant linear technique to decorrelate the two-channel input signals and thus avoid the undesirable nonlinear distortions. We derive two low-complexity adaptive algorithms based on the two-channel gradient lattice algorithm. The models assume the input sequences to the adaptive filters to be autoregressive (AR) processes whose orders are much lower than the lengths of the adaptive filters. This results in an algorithm, whose complexity is only slightly higher than the Normalized Least-Mean-Squares (NLMS) algorithm; the simplest adaptive filtering method. Simulation results show that the proposed algorithms perform favorably when compared with the state-of-the-art algorithms. Finally, we investigate the impact of signal nonlinearity on the convergence behavior of stereophonic acoustic echo cancelers. Simulation studies reveal that application of certain classes of nonlinearities to the two-channel LMS/Newton algorithms helps to reduce the interchannel correlation and further improve the misalignment obtained by these algorithms. But it also leads to an unexpected and significant reduction in the rate of convergence of the mean-square error. In the last segment of the dissertation, we provide an analysis of the two-channel LMS/Newton algorithm that was proposed in our earlier work. Based on the perspectives gained through this analysis, we provide a theoretical understanding for the appearance of the slow modes of convergence in the presence of nonlinearities and show that they can be resolved through a preprocessing step