Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
On-line identification of echo-path impulse responses by Haar-wavelet-based adaptive filter
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Wavelet-based linear system modeling and adaptive filtering
IEEE Transactions on Signal Processing
Proportionate adaptive algorithms for network echo cancellation
IEEE Transactions on Signal Processing
A class of sparseness-controlled algorithms for echo cancellation
IEEE Transactions on Audio, Speech, and Language Processing
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The µ-law proportionate normalized least mean square (MPNLMS) algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS) algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.