Adaptive signal processing
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Adaptive system identification and signal processing algorithms
Adaptive system identification and signal processing algorithms
Application of the leaky extended LMS (XLMS) algorithm in stereophonic acoustic echo cancellation
Signal Processing - Special issue on acoustic echo and noise control
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Topics in Acoustic Echo and Noise Control: Selected Methods for the Cancellation of Acoustical Echoes, the Reduction of Background Noise, and Speech Processing (Signals and Communication Technology)
A fast two-channel projection algorithm for stereophonic acoustic echo cancellation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
IEEE Transactions on Signal Processing
Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Signal Processing
SAR imaging via efficient implementations of sparse ML approaches
Signal Processing
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This paper presents a new class of adaptive filtering algorithms to solve the stereophonic acoustic echo cancelation (AEC) problem in teleconferencing systems. While stereophonic 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. The main reason being the strong cross correlation that exists between the two input audio channels. In the past, nonlinearities have been introduced to reduce this correlation. However, nonlinearities bring with it 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-square (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.