mfcc#
- diffsptk.MFCC#
- class diffsptk.MelFrequencyCepstralCoefficientsAnalysis(mfcc_order, n_channel, fft_length, sample_rate, lifter=1, out_format='y', **fbank_kwargs)[source]#
See this page for details.
- Parameters:
- mfcc_orderint >= 1
Order of MFCC,
.- n_channelint >= 1
Number of mel-filter banks,
.- fft_lengthint >= 2
Number of FFT bins,
.- sample_rateint >= 1
Sample rate in Hz.
- lifterint >= 1
Liftering coefficient.
- f_minfloat >= 0
Minimum frequency in Hz.
- f_maxfloat <= sample_rate // 2
Maximum frequency in Hz.
- floorfloat > 0
Minimum mel-filter bank output in linear scale.
- out_format[‘y’, ‘yE’, ‘yc’, ‘ycE’]
y is MFCC, c is C0, and E is energy.
References
[1]Young et al., “The HTK Book,” Cambridge University Press, 2006.
- forward(x)[source]#
Compute MFCC.
- Parameters:
- xTensor [shape=(…, L/2+1)]
Power spectrum.
- Returns:
- yTensor [shape=(…, M)]
MFCC without C0.
- ETensor [shape=(…, 1)] (optional)
Energy.
- cTensor [shape=(…, 1)] (optional)
C0.
Examples
>>> x = diffsptk.ramp(19) >>> stft = diffsptk.STFT(frame_length=10, frame_period=10, fft_length=32) >>> mfcc = diffsptk.MFCC(4, 8, 32, 8000) >>> y = mfcc(stft(x)) >>> y tensor([[-7.7745e-03, -1.4447e-02, 1.6157e-02, 1.1069e-03], [ 2.8049e+00, -1.6257e+00, -2.3566e-02, 1.2804e-01]])