文件名称:audioretrieval-paper
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The basis for this comparison is a song’ s musical ‘ fingerprint’ , which is computed by
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics.-The basis for this comparison is a song’ s musical ‘ fingerprint’ , which is computed by
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics.
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics.-The basis for this comparison is a song’ s musical ‘ fingerprint’ , which is computed by
clustering a set of spectral features represented by the Mel-Frequency Cepstral Coefficients
(MFCC) of the audio signal. This fingerprint not only uniquely identifies a musical piece it
also provides information about its musical characteristics.
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audioretrieval paper.pdf