Journal Article10.1109/82.826747
The wavelet transform-domain LMS algorithm: a more practical approach
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TL;DR: This work shows how to exploit the redundancy which exists in the computation of the wavelet coefficients between successive iterations so as to significantly reduce the computational load of theWTDLMS algorithm.
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Abstract: The wavelet transform-domain least-mean square (WTDLMS) algorithm is known to have, in general, a faster convergence rate than the time-domain LMS algorithm, and can find many applications in signal processing and communications areas. However, the computational complexity of the wavelet filter bank is relatively high. In this work, we show how to exploit the redundancy which exists in the computation of the wavelet coefficients between successive iterations so as to significantly reduce the computational load of the algorithm.
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Citations
A multi-resolution filtered-x LMS algorithm based on discrete wavelet transform for active noise control
TL;DR: A new active control algorithm based on discrete wavelet transform (DWT) for both stationary and non-stationary noise control that has greatly reduced complexity and a better convergence performance compared to a time domain filtered-x least mean square (TD-FXLMS) algorithm.
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The wavelet transform-domain LMS adaptive filter with partial subband-coefficient updating
TL;DR: This paper proposes and analyzes the wavelet transform-domain LMS (WTDLMS) algorithm where only a subset of the adaptive filter coefficients are updated at each iteration and is tested in the context of system identification and equalization.
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Adaptive Clutter Suppression and Detection Algorithm for Radar Maneuvering Target With High-Order Motions Via Sparse Fractional Ambiguity Function
TL;DR: Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.
A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement
TL;DR: Simulation results prove the efficiency of the proposed WFBSS algorithm in comparison with conventional ones in terms of several objective and subjective criteria and improves robustness of the noise reduction process when compared with the classical two-channel forward symmetric adaptive decorrelating (FSAD) algorithm.
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Complexity considerations for transform-domain adaptive filters
TL;DR: This paper is concerned with the computational complexity and convergence performance of transform-domain adaptive filtering algorithms, and the transform- domain least-mean-square algorithm and the generalized subband decomposition LMS algorithm are considered.
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TL;DR: A guide to using artificial intelligence in the filmmaking process, as well as practical suggestions for improving the quality and efficiency of existing and new approaches.
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Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Fast algorithms for discrete and continuous wavelet transforms
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TL;DR: The goal of this work is to develop guidelines for implementing discrete and continuous wavelet transforms efficiently, and to compare the various algorithms obtained and give an idea of possible gains by providing operation counts.
Transform domain LMS algorithm
TL;DR: In this article, the concept of transform domain adaptive filtering is introduced and the relationship between several existing frequency-domain adaptive filtering algorithms is established, and applications of the discrete Fourier transform (DFT) and the discrete cosine transform (DCT) domain adaptive filter algorithms in the areas of speech processing and adaptive line enhancers are discussed.
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Performance of transform-domain LMS adaptive digital filters
TL;DR: Based on the concept of a self-orthogonalizing algorithm in the transform domain, it is shown that the convergence speed of the TRLMS ADF can be improved significantly for the same excess MSE as that of the L MS ADF.
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