Fast Exact Adaptive Algorithms for Feedforward Active NoiseControlDavid
نویسندگان
چکیده
{ The fast exact least-mean-square (LMS) algorithm is a computationally-eecient method for computing the outputs and updates for an adaptive LMS nite-impulse-response (FIR) lter. In this paper, we extend this method to several useful algorithms for feedforward active noise control: the ltered-X LMS, modiied ltered-X LMS, eecient modiied ltered-X LMS, periodic ltered-X LMS, and sequential ltered-X LMS algorithms, respectively. Choosing a block size of two produces overall behaviors for these fast exact versions that are identical to their non-block counterparts while reducing the numbers of multiplies by up to 25% over those required by the standard algorithms. We then describe Motorola DSP96002 DSP-based implementations of the standard and fast exact versions of the ltered-X LMS algorithm. Our results show that the fast exact implementation can allow a 27.4% increase in the lter lengths over those of the standard implementation on this processor, which is close to the 33.3% increase that would be expected if the number of multiplies were a true indication of an algorithm's complexity.
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Multichannel active noise control algorithms using inverse filters
1.0 Introduction For active noise control (ANC) systems, a common approach is to use adaptive FIR filters trained with the filtered-x LMS algorithm [1], for both feedforward systems and Internal Model Control (IMC) feedback systems, in monochannel or multichannel systems. Variations of the algorithm sometimes called the modified filtered-x LMS algorithm have been published [2], which can achiev...
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