Modeling or compensating a given transfer function is a common task in the field of audio. To comply with the characteristics of hearing, logarithmic frequency resolution filters have been developed, including the Kautz filter, which has orthogonal tap outputs. When the system to be modeled is time-varying, the modeling filter should be tuned to follow the changes in the transfer function. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are well-known methods for adaptive filtering, where the latter has faster convergence rate with lower remaining error, at the expense of high computational demand. In this paper we propose a simplification to the RLS algorithm, which builds on the orthogonality of the tap outputs of Kautz filters, resulting in a significant reduction in computational complexity.
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