回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>我使用两个不同的库提取MFCC功能:</p>
<ul>
<li>python_speech_特性库</li>
<li>鲍勃图书馆</li>
</ul>
<p>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</p>
<p>我的代码的相关部分如下:</p>
<pre><code>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,<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>Energy = 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)
</code></pre>