DocumentCode :
1407290
Title :
Fast LMS/Newton algorithms based on autoregressive modeling and their application to acoustic echo cancellation
Author :
Farhang-Boroujeny, B.
Author_Institution :
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Volume :
45
Issue :
8
fYear :
1997
fDate :
8/1/1997 12:00:00 AM
Firstpage :
1987
Lastpage :
2000
Abstract :
We propose two new implementations of the LMS/Newton algorithm for efficient realization of long adaptive filters. We assume that the input sequence to the adaptive filter can be modeled as an autoregressive (AR) process whose order may be kept much lower than the adaptive filter length. The two algorithms differ in their structural complexity. The first algorithm, which will be an exact implementation of the LMS/Newton algorithm if the AR modeling assumption is accurate, is structurally complicated and fits best into a digital signal processing (DSP)-based implementation. On the other hand, the second algorithm is structurally simple and is tailored more toward very large-scale integrated (VLSI) custom chip design. Analyses of the proposed algorithms are given. It is found that for long filters, both algorithms perform about the same. However for short filters, a noticeable difference between the two may be observed. Simulation results that confirm our theoretical findings are given. Moreover, experiments with speech signals for modeling the acoustics of an office room show the superior convergence of the proposed algorithms when compared with the normalized LMS algorithm
Keywords :
Newton method; VLSI; acoustic signal processing; adaptive filters; adaptive signal processing; application specific integrated circuits; architectural acoustics; autoregressive processes; digital signal processing chips; filtering theory; least mean squares methods; speech processing; AR process; DSP based implementation; VLSI custom chip design; acoustic echo cancellation; adaptive filter length; autoregressive modeling; digital signal processing; experiments; fast LMS/Newton algorithms; input sequence; long adaptive filters; normalized LMS algorithm; office room acoustics modeling; short filters; simulation results; speech signals; structural complexity; Acoustics; Adaptive filters; Algorithm design and analysis; Chip scale packaging; Convergence; Digital signal processing chips; Least squares approximation; Signal processing algorithms; Speech; Very large scale integration;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.611195
Filename :
611195
Link To Document :
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