The fast affine projection algorithm

Tavathia, S.

doi:10.1109/icassp.1995.479482

Cited by 241 publications

(72 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The fast RLS algorithm allows reduction in the complexity of the matrix inversion, resulting in ; however, it shows numerical instability [2]. If , then , where , and the first column of the matrix is only required for calculating in (6).…”

Section: Fap Algorithmmentioning

confidence: 99%

“…The FAP algorithm is defined as follows [2], [5]. Let the system output be , where is the excitation signal matrix, is an unknown impulse response, and is additive white noise, and assume that for ,wehave…”

Section: Fap Algorithmmentioning

confidence: 99%

“…However, it is complicated for implementation. The fast AP (FAP) algorithm allows a significant simplification [2], but it requires matrix inversion, which is a source of numerical instability, especially when implementing on realtime hardware [e.g., field-programmable gate array (FPGA)] and fixed-point software [e.g., digital signal processor (DSP)] platforms. In the original FAP algorithm, this is due to the fast recursive least-squares (RLS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coordinate descent iterations in fast affine projection algorithm

Zakharov

Albu

2005

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.

show abstract

Section: Fap Algorithmmentioning

confidence: 99%

Section: Fap Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coordinate descent iterations in fast affine projection algorithm

Zakharov

Albu

2005

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

show abstract

“…One of the most popular is the normalized least-mean-square (NLMS) algorithm. Also, the affine projection algorithm (APA) [originally proposed in (12)] and some of its versions, e.g., [13], [14], were found to be very attractive choices for echo cancellation. However, there is still a need to improve the performance of these algorithms for echo cancellation.…”

Section: Echo Cancellationmentioning

confidence: 99%

Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation

Samalla¹,

Rao²,

Stayanarayana³

2013

IJIGSP

View full text Add to dashboard Cite

Abstract-This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.

show abstract

“…SUBBAND ADAPTIVE FILTERING: When Fast APA is used and the subband signals are real-valued, the computational requirement in this step is 2M+20P real multiplications per sample [7]. Here M is the adaptive filter order and P is the projection order.…”

Section: Iiicomputational Complexity Analysismentioning

confidence: 99%

Real-valued delayless subband affine projection algorithm for acoustic echo cancellation

Huang

Kyriakakis

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

View full text Add to dashboard Cite

-Acoustic Echo Cancellation (AEC) often involves adaptive filters with large numbers of taps, which results in poor performance in real-time applications. The utilization of Delayless Subband Adaptive Filter (DSAF) helps reduce computations and improve the overall performance. However, conventional oversampled subband adaptive filters mainly use DFT or GDFT based analysis/synthesis filter banks and generate "complex-valued" subband signals. This is particularly inefficient when applying the Affine Projection Algorithm (APA), a popular adaptive algorithm for AEC problem, to each subband. For APA implementation, real-valued signals show higher efficiency than complex signals. In this paper, we present a real-valued delayless subband APA and study both its computational complexity and performance on AEC problems. Compared to the complex valued approach, our method achieves a better performance with lower computational cost.

show abstract

The fast affine projection algorithm

Cited by 241 publications

References 5 publications

Coordinate descent iterations in fast affine projection algorithm

Coordinate descent iterations in fast affine projection algorithm

Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation

Real-valued delayless subband affine projection algorithm for acoustic echo cancellation

Contact Info

Product

Resources

About