Abstract
Recently, a fast version of LMS algorithm where only a small subset of the coefficients is updated at each iteration has been published in the literature. In this work, we analyze the effects of this technique on the discrete cosine transform domain LMS (DCTLMS) algorithm, and highlight its advantages and drawbacks. It is shown, in particular, that a reduction in the computational complexity can be achieved without causing any degradation to the steady state error of the algorithm. The analytical results are then confirmed by simulations where real speech and first-order Markov signals are used.
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