A Clipping-Based Selective-Tap Adaptive Filtering Approach to Stereophonic Acoustic Echo Cancellation

Tarbiat Modares Univ., Tehran, Iran
IEEE Transactions on Audio Speech and Language Processing (Impact Factor: 2.48). 09/2011; 19(6):1826 - 1836. DOI: 10.1109/TASL.2010.2102752
Source: IEEE Xplore


Stereophonic acoustic echo cancellation remains one of the challenging areas for tele/video-conferencing applications. However, the existence of high interchannel coherence between the two input signals for such systems leads to considerable degradation in misalignment convergence of the adaptive filters. We propose a new algorithm for improving the convergence performance and steady-state misalignment by considering robustness to the source position in the transmission room. We achieve this by exploiting the inherent decorrelating properties of selective-tap adaptive filtering as well as employing a variable clipping threshold for the unselected taps. Simulation results using colored noise and speech signals show an improvement over existing algorithms both in terms of convergence rate as well as steady-state normalized misalignment.

Download full-text


Available from: Mojtaba Lotfizad, Sep 29, 2015
31 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: One of the efficient solutions for the identification of long finite-impulse response systems is the three-level clipped input LMS/RLS (CLMS/CRLS) adaptive filter. In this paper, we first derive the convergence behavior of the CLMS and CRLS algorithms for both time-invariant and time-varying system identification. In addition, we employ results arising from this analysis to derive the optimal step-size and forgetting factor for CLMS and CRLS. We show that these optimal step-size and forgetting factor allow the algorithms to achieve a low steady-state misalignment.
    Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Rapid advances in the field of signal processing are revolutionizing algorithms. This paper describes the concept of adaptive noise cancellation, an alternative method of estimating signals corrupted by additive noise or interference. The Adaptive algorithms are used to improve the convergence rate, signal to noise ratio, stability, mean square error, steady state behavior, tracking, misadjustment has become a focus on digital signal processing. Accurate cancellation of noise in signal processing is a key step of adaptive filter algorithms. In this paper, Acoustic echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their comparative results. The results shown are using some specific algorithms. The results show, improving convergence rate with less no of taps is the most difficult phase in signal processing applications for the perfect working of any system.
    Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: An echo canceller with both double-talk detector (DTD) and echo path change detector (PCD) is put forward. Double-talk detector is based on the activity detection of speech, using sub-filter to detect the near end talk, and enhancing its performance by adding a sliding window. The path change detector works by comparing the performance between main-filter and sub-filter. There is a problem when an echo canceller has both detectors: It's hard to discriminate between echo and near end speech after path changes, which can cause the echo of far voice increase suddenly and this echo is similar to the near end voice. To solve this problem, we propose to change double-talk detection threshold after path change happened. In addition, using the short silence mute at the beginning of a call, some random sequences will be sent to initialize the filter. The simulation results show that the proposed echo canceller has a satisfactory performance during double-talk and path change.
    International Conference on Automatic Control and Artificial Intelligence (ACAI 2012); 01/2012
Show more