Open Access

Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications

  • Yan Wu Jennifer1Email author,
  • John Homer2,
  • Geert Rombouts3 and
  • Marc Moonen3
EURASIP Journal on Audio, Speech, and Music Processing20072007:071495

DOI: 10.1155/2007/71495

Received: 9 July 2006

Accepted: 18 February 2007

Published: 12 April 2007

Abstract

In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA) or the low-complexity version, the fast affine projection (FAP) algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled) channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.

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Authors’ Affiliations

(1)
Canberra Research Laboratory, National ICT Australia and Research School of Information Science and Engineering, The Australian National University
(2)
School of Information Technology and Electrical Engineering, The University of Queensland
(3)
Departement Elektrotechniek, Katholieke Universiteit Leuven, ESAT/SCD

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Copyright

© Yan Wu Jennifer et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.