Calculate MFCC/Fbank feature for wav files
Support python 3.6 only!
To use, make sure you have install SCIPY lib then import MFCC modual by:
import mfcc
Simply run the following command for MFCC feature:
mfcc.calcMFCC(signal, sample_rate=16000, win_length=0.025,
win_step=0.01, filters_num=26, NFFT=512,
low_freq=0, high_freq=None, pre_emphasis_coeff=0.97,
cep_lifter=22, append_energy=True, append_delta=False)
Arguments:
signal: 1-D numpy array.
sample_rate: Sampling rate. Defaulted to 16KHz.
win_length: Window length. Defaulted to 0.025, which is 25ms/frame.
win_step: Interval between the start points of adjacent frames. Defaulted to 0.01, which is 10ms.
filters_num: Numbers of filters. Defaulted to 26.
NFFT: Size of FFT. Defaulted to 512.
low_freq: Lowest frequency.
high_freq: Highest frequency.
pre_emphasis_coeff: Coefficient for pre-emphasis. Pre-emphasis increase the energy of signal at higher frequency. Defaulted to 0.97.
cep_lifter: Numbers of lifter for cepstral. Defaulted to 22.
append_energy: Whether to append energy. Defaulted to True.
append_delta: Whether to append delta to feature. Defaulted to False.
Returns:
2-D numpy array with shape (NUMFRAMES, features). Each frame containing filters_num of features.
Run the following command for Fbank feature:
mfcc.calcFbank(signal, sample_rate=16000, win_length=0.025,
win_step=0.01, filters_num=26, NFFT=512,
low_freq=0, high_freq=None, pre_emphasis_coeff=0.97,
append_energy=True, append_delta=False):
Arguments:
signal: 1-D numpy array.
sample_rate: Sampling rate. Defaulted to 16KHz.
win_length: Window length. Defaulted to 0.025, which is 25ms/frame.
win_step: Interval between the start points of adjacent frames. Defaulted to 0.01, which is 10ms.
filters_num: Numbers of filters. Defaulted to 26.
NFFT: Size of FFT. Defaulted to 512.
low_freq: Lowest frequency.
high_freq: Highest frequency.
pre_emphasis_coeff: Coefficient for pre-emphasis. Pre-emphasis increase the energy of signal at higher frequency. Defaulted to 0.97.
append_energy: Whether to append energy. Defaulted to True.
append_delta: Whether to append delta to feature. Defaulted to False.
Returns:
2-D numpy array with shape (NUMFRAMES, features). Each frame containing filters_num of features.