Selective coefficient update of gradient-based adaptive algorithms

TitleSelective coefficient update of gradient-based adaptive algorithms
Publication TypeConference Paper
Year of Publication1997
AuthorsAboulnasr, T., and K. Mayyas
Conference NameAcoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Pagination1929 -1932 vol.3
Date Publishedapr.
Keywordsacoustic echo cancellation, acoustic signal processing, adaptive filter coefficients, adaptive filters, adaptive signal processing, complexity reduction, computational complexity, computational overhead reduction, correlated inputs, echo suppression, filtering theory, gradient-based adaptive algorithms, iteration, iterative methods, least mean squares methods, mean square error, NLMS algorithm, normalized LMS algorithm, selective coefficient update

One common approach to reducing the computational overhead of the normalized LMS (NLMS) algorithm is to update a subset of the adaptive filter coefficients. It is known that the mean square error (MSE) is not equally sensitive to the variations of the coefficients. Accordingly, the choice of the coefficients to be updated becomes crucial. On this basis, we propose an algorithm that belongs to the same family but selects at each iteration a specific subset of the coefficients that will result in the largest reduction in the performance error. The proposed algorithm reduces the complexity of the NLMS algorithm, as do the current algorithms from the same family, while maintaining a performance close to the full update NLMS algorithm specifically for correlated inputs


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