def ssc(signal,
samplerate=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):
'''
待补充
'''
high_freq = high_freq or samplerate / 2
signal = pre_emphasis(signal, pre_emphasis_coeff)
frames = audio2frame(signal, win_length * samplerate,
win_step * samplerate)
spec_power = spectrum_power(frames, NFFT)
spec_power = numpy.where(spec_power == 0,
numpy.finfo(float).eps, spec_power) #能量谱
fb = get_filter_banks(filters_num, NFFT, samplerate, low_freq, high_freq)
feat = numpy.dot(spec_power, fb.T) #计算能量
R = numpy.tile(
numpy.linspace(1, samplerate / 2, numpy.size(spec_power, 1)),
(numpy.size(spec_power, 0), 1))
return numpy.dot(spec_power * R, fb.T) / feat
def fbank(signal,
samplerate=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):
"""Perform pre-emphasis -> framing -> get magnitude -> FFT -> Mel Filtering.
Args:
signal: 1-D numpy array.
samplerate: 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.
cep_num: Numbers of cepstral coefficients. Defaulted to 13.
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.
Returns:
feat: Features.
energy: Energy.
"""
# Calculate the highest frequency.
high_freq = high_freq or samplerate / 2
# Pre-emphasis
signal = pre_emphasis(signal, pre_emphasis_coeff)
# rames: 2-D numpy array with shape (frame_num, frame_length)
frames = audio2frame(signal, win_length * samplerate,
win_step * samplerate)
# Caculate energy and modify all zeros to eps.
spec_power = spectrum_power(frames, NFFT)
energy = numpy.sum(spec_power, 1)
energy = numpy.where(energy == 0, numpy.finfo(float).eps, energy)
# Get Mel filter banks.
fb = get_filter_banks(filters_num, NFFT, samplerate, low_freq, high_freq)
# Get MFCC and modify all zeros to eps.
feat = numpy.dot(spec_power, fb.T)
feat = numpy.where(feat == 0, numpy.finfo(float).eps, feat)
return feat, energy