Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Step-size control for acoustic echo cancellation filter—an overview
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Adaptive algorithms for sparse echo cancellation
Signal Processing
Plant identification via adaptive combination of transversal filters
Signal Processing - Signal processing in UWB communications
Adaptive Filters
Improving the Tracking Capability of Adaptive Filters via Convex Combination
IEEE Transactions on Signal Processing - Part II
Complexity reduction of the NLMS algorithm via selectivecoefficient update
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
Proportionate adaptive algorithms for network echo cancellation
IEEE Transactions on Signal Processing
On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk
IEEE Transactions on Audio, Speech, and Language Processing
Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms
IEEE Transactions on Audio, Speech, and Language Processing
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
Transient and steady-state analysis of the affine combination of two adaptive filters
IEEE Transactions on Signal Processing
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Proportionate adaptive filters, such as those based on the improved proportionate normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo cancellation as an interesting alternative to the normalized least-mean-square (NLMS) filter. Proportionate schemes offer improved performance when the echo path is sparse, but are still subject to some compromises regarding their convergence properties and steady-state error. In this paper, we study how combination schemes, where the outputs of two independent adaptive filters are adaptively mixed together, can be used to increase IPNLMS robustness to channels with different degrees of sparsity, as well as to alleviate the rate of convergence versus steady-state misadjustment tradeoff imposed by the selection of the step size. We also introduce a new block-based combination scheme which is specifically designed to further exploit the characteristics of the IPNLMS filter. The advantages of these combined filters are justified theoretically and illustrated in several echo cancellation scenarios.