Advances in Network and Acoustic Echo Cancellation

Benesty, Jacob; Gnsler, T.; Morgan, Dp; Sondhi, M. M.

doi:10.1007/978-3-662-04437-7

Cited by 453 publications

(236 citation statements)

References 0 publications

Supporting

Mentioning

233

Contrasting

Unclassified

Order By: Relevance

“…For reducing M , the performance of MMax-AP degrades gracefully compared to fully updated AP. (12)- (14). For M = L/2 and M = L/4, the performance of MMax-RLS is close to that of the RLS algorithm.…”

Section: A Single Channel Aecmentioning

confidence: 80%

See 1 more Smart Citation

Affine projection and recursive least squares adaptive filters employing partial updates

Naylor

Khong

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

View full text Add to dashboard Cite

Abstract-We present order K afne projection and recursive least squares adaptive lters employing partial update schemes. The starting point of the work is the MMax tap-selection criterion in which, given a lter length L, only M coefcients are updated that correspond to the M largest magnitude elements of the regression vector. We extend this approach from its existing form of MMax-NLMS to new afne projection and recursive least squares schemes with supporting analysis and simulation results. We discuss the computational complexity of these approaches for two alternative sort procedures. Finally, we extend the MMax criterion to a multichannel case by introducing an exclusivity constraint and show the effectiveness of the resulting XM tapselection criterion for application to stereophonic acoustic echo cancellation.

show abstract

Section: A Single Channel Aecmentioning

confidence: 80%

“…In both MMax-AP and MMax-RLS simulations, white Gaussian noise (WGN) input sequence were used. Figure 5 shows an example result of stereophonic AEC in which we incorporated the non-linear preprocessor [14] to both the XM-RLS (XMNL-RLS) and RLS algorithms (NL-RLS). For clarity, the misalignment of only one channel is plotted.…”

Section: A Single Channel Aecmentioning

confidence: 99%

Affine projection and recursive least squares adaptive filters employing partial updates

Naylor

Khong

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

View full text Add to dashboard Cite

show abstract

“…The original idea of this application appeared in the sixties [9], and it can be considered as a real milestone in telecommunication systems. A general scheme for echo cancellation is depicted in Figure. cancellation contexts [10] it is interesting to notice that the scheme from Figure.1 can be interpreted as a combination of two classes of adaptive system configurations, according to the adaptive filter theory [11]. First, it represents a -system identification‖ configuration because the goal is to identify an unknown system (i.e., the echo path) with its output corrupted by an apparently -undesired‖ signal (i.e., the near-end signal).…”

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

“…; c LÀ1 g. Typical values for the algorithm constants are given as d p ¼ 0:01 and r ¼ 5=L. An important modification to this algorithm is made in [11] and [12] in which (6) is rewritten as…”

Section: Proportionate Normalized Lmsmentioning

confidence: 99%

Adaptive algorithms for sparse echo cancellation

2006

View full text Add to dashboard Cite

The cancellation of echoes is a vital component of telephony networks. In some cases the echo response that must be identified by the echo canceller is sparse, as for example when telephony traffic is routed over networks with unknown delay such as packet-switched networks. The sparse nature of such a response causes standard adaptive algorithms including normalized LMS to perform poorly. This paper begins by providing a review of techniques that aim to give improved echo cancellation performance when the echo response is sparse. In addition, adaptive filters can also be designed to exploit sparseness in the input signal by using partial update procedures. This concept is discussed and the MMax procedure is reviewed. We proceed to present a new high performance sparse adaptive algorithm and provide comparative echo cancellation results to show the relative performance of the existing and new algorithms. Finally, an efficient low cost implementation of our new algorithm using partial update adaptation is presented and evaluated. This algorithm exploits both sparseness of the echo response and also sparseness of the input signal in order to achieve high performance without high computational cost. r

show abstract

Advances in Network and Acoustic Echo Cancellation

Cited by 453 publications

References 0 publications

Affine projection and recursive least squares adaptive filters employing partial updates

Affine projection and recursive least squares adaptive filters employing partial updates

Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation

Adaptive algorithms for sparse echo cancellation

Contact Info

Product

Resources

About