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Code of speech signal endpoint detection using subband spectral entropy method
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<span style="color:#555555;font-family:"font- size:15px;background-color : ffffff; & quot; & gt; using subband spectral entropy method for speech signal endpoint detection code, derived from http://blog.csdn.net/u010451580/article/details/51192281
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The solution of MFCC
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Using MATLAB to get the MFCC features of speech, using the most conventional method, there is no settlement of first-order and second-order
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Extracting feature parameters
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%In VQ based speaker recognition system, vector quantization plays a dual role. In the training phase, the feature parameters extracted by each speaker are classified to generate codebooks composed of different codewords. In the recognition (matching) stage, we use VQ method to calculate the average distortion measure (the system uses Euclidean distance measure when calculating the distance d), so as to judge who the speaker is. This code is to train the audio, that is to extract feature parameters
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Filter for determining matrix
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In VQ based speaker recognition system, vector quantization plays a dual role. In the training phase, the feature parameters extracted by each speaker are classified to generate codebooks composed of different codewords. In the recognition (matching) stage, we use VQ method to calculate the average distortion measure (the system uses Euclidean distance measure when calculating the distance d), so as to judge who the speaker is. This code is to determine the matrix filter
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