Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Nonlinear acoustic echo cancellation with 2nd order adaptive Volterra filters
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Baseband Volterra filters for implementing carrier basednonlinearities
IEEE Transactions on Signal Processing
Adaptive parallel-cascade truncated Volterra filters
IEEE Transactions on Signal Processing
A family of adaptive filter algorithms with decorrelatingproperties
IEEE Transactions on Signal Processing
Genetic algorithm based identification of nonlinear systems bysparse Volterra filters
IEEE Transactions on Signal Processing
Adaptive Volterra filters for active control of nonlinear noiseprocesses
IEEE Transactions on Signal Processing
Polyphase Representation of Multirate Nonlinear Filters and Its Applications
IEEE Transactions on Signal Processing
On the performance of adaptive pruned Volterra filters
Signal Processing
A method for removing noise from continuous brain signal recordings
Computers and Electrical Engineering
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In most practical applications, the major drawback for using adaptive Volterra filters is the large number of coefficients to cope with. Several research works discussing strategies to reduce the computational burden of these structures have been presented in the open literature. For such, a common approach has been the use of some type of sparseness in Volterra filter kernels. In this work, a sparse-interpolated approach, with the interpolation having the purpose of recreating (in an approximate way) the elements disregarded for obtaining sparse kernels, is presented and discussed. Thus, for the adaptive sparse-interpolated Volterra filter, coefficient update expressions considering both least-meansquare (LMS) and normalized LMS (NLMS) algorithms are derived by using a constrained approach. In general, the proposed strategy outperforms other sparse schemes in terms of the tradeoff between computational complexity and mean-square error (MSE) performance, as shown through numerical simulations.