A fast exact weighted subband adaptive algorithm and its application to mono and stereo acoustic echo cancellation

Mayyas, K.

doi:10.1016/j.jfranklin.2004.10.002

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…(35), where Q(k) is the diagonal weighting matrix. This aim is obtained by the following weight vector update equation [32]:…”

Section: Set-membership Ipnsaf (Sm-ipnsaf) Algorithmmentioning

confidence: 99%

“…In these applications a large number of operations are needed for their implementation and hence presenting a slow convergence rate when using LMS-based algorithms [33]. In [34,35], the subband adaptive algorithm (SAF) called normalized SAF (NSAF) was developed based on a constrained optimization problem. The filter update equation proposed in [34,35] is similar to that proposed in [33,36], where the fullband filters are updated instead of subfilters as in the conventional SAF structure [37,38].…”

Section: Introductionmentioning

confidence: 99%

“…In [34,35], the subband adaptive algorithm (SAF) called normalized SAF (NSAF) was developed based on a constrained optimization problem. The filter update equation proposed in [34,35] is similar to that proposed in [33,36], where the fullband filters are updated instead of subfilters as in the conventional SAF structure [37,38]. In [26], the concepts of SPU and SM adaptive filtering were extended to the NSAF, and the SPU-NSAF and SM-NSAF algorithms were presented.…”

Section: Introductionmentioning

confidence: 99%

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A family of proportionate normalized subband adaptive filter algorithms

Abadi

Kadkhodazadeh

2011

Journal of the Franklin Institute

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“…(35), where Q(k) is the diagonal weighting matrix. This aim is obtained by the following weight vector update equation [32]:…”

Section: Set-membership Ipnsaf (Sm-ipnsaf) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A family of proportionate normalized subband adaptive filter algorithms

Abadi

Kadkhodazadeh

2011

Journal of the Franklin Institute

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“…Among all the filter banks, the cosine-modulated filter bank (CMFB) is one of the most frequently used filter banks in audio coding [28][29][30], adaptive signal processing [31,32], and ECG signal processing [33][34][35][36], because of its matching properties of the analysis filter bank to the characteristics of the input signal. In ECG signal processing, CMFB filter banks are used in several applications such as beat detection, signal enhancement, beat classification, and compression.…”

Section: Application To Sub-band Codingmentioning

confidence: 99%

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Kumar

Singh

Anand

2011

Journal of the Franklin Institute

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“…(ii) Previous attempts to perform adaptive filtering in subbands encounter aliasing and band-edge effects [16], [27], [34]- [36], [55], [111]. These structural problems can be solved by having the modeling transversal filter to operate on the subband signals at the original sampling rate [13], [14], [61], [62], [77], [78], [99]. The fullband tap weights of the modeling filter are adapted, at a decimated rate, using the subband signals that have been normalized by their respective subband input variance.…”

Section: Motivation Of Thesismentioning

confidence: 99%

Subband adaptive algorithms for high-order transversal filters

Lee¹

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Adaptive filters have been employed in wide range of applications where signal characteristics are not available, or are time-varying, such as acoustic echo cancellation and adaptive noise cancellation. Despite their widespread use, performance of the adaptive filters is degraded in some applications by two major factors, namely, (i) coloring of the input signal, and (ii) long impulse response of the unknown system. The classical least-mean-square (LMS) algorithm is computationally efficient but its convergence speed deteriorates as the eigenvalue spread of the input autocorrelation matrix increases due to the coloring of the input signal. The convergence problem worsens when the order of the adaptive filter is increased in order to model the unknown system with sufficient accuracy. In contrast, recursive least-squares (RLS) algorithm is adequate in dealing with colored excitation with large spectral dynamic range. Nevertheless, high computational complexity renders the algorithm not applicable in real-time for the case of high-order adaptive filter. Those problems have motivated various approaches of employing subband and multirate techniques in designing computationally efficient adaptive algorithms with improved convergence performance against high eigenvalue disparity. Conventional subband adaptive filters (SAFs) decompose the input signal and desired response into multiple contiguous spectral bands, decimate the subband signals, process each subband with separate adaptive subfilters, and finally interpolate and recombine the resulting subband signals to obtain a filtered output. Intuitively, faster convergence is possible because the spectral dynamic range is greatly reduced in each subband. Furthermore, the computational burden can be reduced by decimating both the order and adaptation rate of the subfilters. Yet, detailed analysis shows that the convergence ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library viii rate of the conventional SAF is limited by aliasing and band-edge effects, in spite of a number of modifications that have been proposed, such as, introducing adaptive cross-filters or spectral gaps between adjacent subfilters, as well as oversampled scheme. This thesis describes, analyzes, and generalizes a new class of SAFs, called the normalized SAF (NSAF), whereby the adaptive filter is no longer separated into subfilters. Instead, subband signals, which are normalized by their respective subband input variance, are used to adapt the fullband tap weights of a modeling filter. The

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A fast exact weighted subband adaptive algorithm and its application to mono and stereo acoustic echo cancellation

Cited by 11 publications

References 16 publications

A family of proportionate normalized subband adaptive filter algorithms

A family of proportionate normalized subband adaptive filter algorithms

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Subband adaptive algorithms for high-order transversal filters

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