Cepstrum

1
Cepstrum

• Cepstrum ? homomorphic transformation
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2
Feature Extraction

• Major steps
• Overlapping frames of 256 points (32 ms)
• Hamming windowing to lessen distortion
• cepstrum(frame) real(IFFT(logFFT(frame)))
• FFT Fast Fourier Transform
• IFFT Inverse FFT
• A feature vector consists of the first 14
  cepstral coefficients of a frame.
• Optional steps
• Frequency-selective filter to reduce noise
• Mel-warped cepstral coefficients
• Feature-wise normalization

3
Physical Meanings of Cepstrum
4
Feature Extraction
2.39 sec. speech signal
148 frames of 256 points
Hamming windowing
take frames
low-pass filter
FFT
abs
log
resample
IFFT
real
first 14 coefficients
normalization
148 feature vectors of length 14
5
Feature Extraction

• Upper speaker 1 , lower speaker 2