文件列表:

NormalizeFilterGain.m (363, 2009-04-12)
PNCC.m (9693, 2009-04-19)
ComputeFilterResponse.m (7712, 2009-04-12)
SpectrogramDemo\ComputeFilterResponse.m (7712, 2009-04-12)
SpectrogramDemo\DemoBatch.asv (3014, 2009-04-19)
SpectrogramDemo\DemoBatch.m (3105, 2009-04-19)
SpectrogramDemo\NormalizeFilterGain.m (363, 2009-04-12)
SpectrogramDemo\PNCCForDemo.asv (8955, 2009-04-19)
SpectrogramDemo\PNCCForDemo.m (9699, 2009-04-19)
SpectrogramDemo\sb01_Clean.sph (165888, 2007-08-30)
SpectrogramDemo\sb01_Music_05dB.sph (165888, 2007-06-28)
SpectrogramDemo\sb01_Street_05dB.sph (165888, 2008-09-08)
SpectrogramDemo\sb01_White_05dB.sph (165888, 2007-06-28)

Programmed by Chanwoo Kim for Interspeech 2009 Apr 10, 2009 1) Just Run PNCC('outFile', 'inPutFile') IMPORTANT : The input is assumed in the single-Channel sphere NIST format. The sampling rate should be "16 kHz". It does not check the header, but just skips the header. We used this program in getting result in INTERSPEECH 2009 paper on the RM1 database 2) Output is a feature in Sphinx format 3) To see the spectrogram demo Go to SpectrogramDemo directory Type DemoBatch.m in Matlab It will launch three figures: a) spectrogram, b) spectrogram using log nonliearity (This one is still better than MFCC in our recognition experiment) c) gammatone weighting, PNCC (This one is much better than b)) In each figure you will sub-figures obtained from different noisy conditions. If the spectrogram from the noisy condition is closer to the top subfigure in each figure, then we can think that this feature is more robust.

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