文件名称:SPEECHX
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. The normalized least mean square is the most popular due to its simplicity. The stability of the basic NLMS is controlled by a fixed step size. This parameter also governs the rate of convergence, speed of tracking ability and the amount of steady-state excess mean-square error. The main aim of this paper is to reduce the low excess MSE associated with the conventional NLMS, a number of variable step-size NLMS (VSS-NLMS) algorithms have been presented in the past two decades. In this we compare the result with previous one.
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下载文件列表
SPEECHX/fwseg_noise.m
SPEECHX/fwseg_noise1.m
SPEECHX/pearson.m
SPEECHX/readme.docx
SPEECHX/readme.pdf
SPEECHX/SNRLESC.m
SPEECHX/SNRloss1.m
SPEECHX/snrloss_spcom.pdf
SPEECHX/S_03_01.wav
SPEECHX/S_03_01_babble_sn0_klt.wav
SPEECHX/S_03_01_babble_sn0_klt_n.wav
SPEECHX
SPEECHX/fwseg_noise1.m
SPEECHX/pearson.m
SPEECHX/readme.docx
SPEECHX/readme.pdf
SPEECHX/SNRLESC.m
SPEECHX/SNRloss1.m
SPEECHX/snrloss_spcom.pdf
SPEECHX/S_03_01.wav
SPEECHX/S_03_01_babble_sn0_klt.wav
SPEECHX/S_03_01_babble_sn0_klt_n.wav
SPEECHX
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