Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Natural gradient works efficiently in learning
Neural Computation
Advances in Network and Acoustic Echo Cancellation
Advances in Network and Acoustic Echo Cancellation
Complexity reduction of the NLMS algorithm via selectivecoefficient update
IEEE Transactions on Signal Processing
Convergence of exponentiated gradient algorithms
IEEE Transactions on Signal Processing
Step size bound of the sequential partial update LMS algorithm with periodic input signals
EURASIP Journal on Audio, Speech, and Music Processing
Adaptive combination of proportionate filters for sparse echo cancellation
IEEE Transactions on Audio, Speech, and Language Processing
Improved adaptive filtering schemes via adaptive combination
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
A PNLMS algorithm with individual activation factors
IEEE Transactions on Signal Processing
Stochastic model for the mean weight evolution of the IAF-PNLMS algorithm
IEEE Transactions on Signal Processing
Adaptive mixture methods based on Bregman divergences
Digital Signal Processing
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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.