期刊
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING
卷 8, 期 5, 页码 606-618出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/89.861382
关键词
fast convergence algorithms; multichannel active noise control; transaural sound reproduction
In the last ten years, there has been much research on active noise control (ANC) systems and transaural sound reproduction (TSR) systems. In those fields, multichannel FIR adaptive filters are extensively used. For the learning of FIR adaptive filters, recursive-least-squares (RLS) algorithms are known to produce a faster convergence speed than stochastic gradient descent techniques, such as the basic least-mean-squares (LMS) algorithm or even the fast convergence Newton-LMS, the gradient-adaptive-lattice (GAL) LMS and the discrete-cosine-transform (DCT) LMS algorithms. In this paper, multichannel RLS algorithms and multichannel fast-transversal-filter (FTF) algorithms are introduced, with the structures of some stochastic gradient descent algorithms used in ANC: the filtered-x LMS, the modified filtered-x LMS and the adjoint-LMS. The new algorithms can be used in ANC systems or for the deconvolution of sounds in TSR systems. Simulation results comparing the convergence speed, the numerical stability and the performance using noisy plant models for the different multichannel algorithms will be presented, showing the large gain of convergence speed that can be achieved by using some of the introduced algorithms.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据