Multichannel acoustic signal processing
Creators: name: Rao, Harsha I. K
Publisher: name: University of Utah
Year: 2012
Subjects: Acoustic signal processing
Multichannel audio
Sound realism
Sound spatialization
3-D audio system

description: Multichannel 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.