我使用两个不同的库提取MFCC功能:
但是两者的输出是不同的,甚至形状也不一样。这正常吗?或者我缺少一个参数?在
我的代码的相关部分如下:
import bob.ap
import numpy as np
from scipy.io.wavfile import read
from sklearn import preprocessing
from python_speech_features import mfcc, delta, logfbank
def bob_extract_features(audio, rate):
#get MFCC
rate = 8000 # rate
win_length_ms = 30 # The window length of the cepstral analysis in milliseconds
win_shift_ms = 10 # The window shift of the cepstral analysis in milliseconds
n_filters = 26 # The number of filter bands
n_ceps = 13 # The number of cepstral coefficients
f_min = 0. # The minimal frequency of the filter bank
f_max = 4000. # The maximal frequency of the filter bank
delta_win = 2 # The integer delta value used for computing the first and second order derivatives
pre_emphasis_coef = 0.97 # The coefficient used for the pre-emphasis
dct_norm = True # A factor by which the cepstral coefficients are multiplied
mel_scale = True # Tell whether cepstral features are extracted on a linear (LFCC) or Mel (MFCC) scale
c = bob.ap.Ceps(rate, win_length_ms, win_shift_ms, n_filters, n_ceps, f_min,
f_max, delta_win, pre_emphasis_coef, mel_scale, dct_norm)
c.with_delta = False
c.with_delta_delta = False
c.with_energy = False
signal = np.cast['float'](audio) # vector should be in **float**
example_mfcc = c(signal) # mfcc + mfcc' + mfcc''
return example_mfcc
def psf_extract_features(audio, rate):
signal = np.cast['float'](audio) #vector should be in **float**
mfcc_feature = mfcc(signal, rate, winlen = 0.03, winstep = 0.01, numcep = 13,
nfilt = 26, nfft = 512,appendEnergy = False)
#mfcc_feature = preprocessing.scale(mfcc_feature)
deltas = delta(mfcc_feature, 2)
fbank_feat = logfbank(audio, rate)
combined = np.hstack((mfcc_feature, deltas))
return mfcc_feature
track = 'test-sample.wav'
rate, audio = read(track)
features1 = psf_extract_features(audio, rate)
features2 = bob_extract_features(audio, rate)
print("--------------------------------------------")
t = (features1 == features2)
print(t)
你试过用宽容的眼光来比较这两者吗?我相信这两个mfcc是浮点数数组,测试是否完全相等可能不是明智之举。尝试使用带有一定公差的
numpy.testing.assert_allclose
,并确定该公差是否足够好。在尽管如此,我想你说即使形状不匹配,我没有经验鲍勃.ap自信地对此发表评论。然而,由于窗口化的原因,一些库经常在输入数组的开始或结尾用零填充输入,如果其中一个库的操作方式不同,这可能是造成这种情况的原因。在
是的,有不同种类的算法,每个实现都有自己的风格
它不仅仅是关于参数,还有一些算法上的差异,如窗口形状(hamming vs hanning)、mel过滤器的形状、mel过滤器的开始、mel过滤器的规范化、提升、dct风格等等。在
如果你想要相同的结果,只需使用单一的库进行提取,同步它们是非常无望的。在
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