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MATLAB-LPC

于 2021-04-08 发布文件大小:275KB

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下载积分: 1 下载次数: 19

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matlab实现lpc编解码的程序，很好，很不错，值得看看。。。(matlab 。。。。。lpc。。。。。。。。。。。。。。。。。。。。。。。)

文件列表：

用MATLAB编写的LPC编码器及解码器源代码
.....................................\lpc by matlab
.....................................\.............\aa.wav,102518,2004-12-19
.....................................\.............\main.m,768,2005-10-17
.....................................\.............\mlp.m,4317,2005-10-16
.....................................\.............\proclpc.m,5337,2003-05-12
.....................................\.............\qing.m,4,2005-10-16
.....................................\.............\s1ofwb.wav,93364,2003-05-08
.....................................\.............\s1omwb.wav,86496,2003-05-08
.....................................\.............\s2df.m,768,2006-05-28
.....................................\.............\s2ofwb.wav,96346,2003-05-08
.....................................\.............\s2omwb.wav,89738,2003-05-08
.....................................\.............\speechcoder1.m,1164,2003-05-12
.....................................\.............\speechcoder2.m,1894,2003-05-12
.....................................\.............\synlpc.m,3609,2003-05-12
.....................................\.............\synlpc2.m,3493,2003-05-12
.....................................\.............\Untitled.asv,110,2006-05-28
.....................................\.............\Untitled.m,88,2006-05-28

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In his 1960 famous publication (“A new approach to linear filtering and prediction problems”, Trans.ASME J. Basic Engineering., vol 82, March 1960, pp 34-45), Rudolf Kalman based the construction of the state estimation filter on probability theory, and more specifically, on the properties of conditional Gaussian random variables. The criterion he proposed to minimize is the state vector covariance norm, yielding to the classical recursion : the new state estimate is deduced from the previous estimation by addition of a correction term proportional to the prediction error (or the innovation of the measured signal).

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