Question

我正在使用两个不同的库来提取MFCC功能：

python_speech_features库
BOB库

但是，两者的输出不同，甚至形状也不相同。那是正常的吗？还是我缺少一个参数？

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)

Answer 1

您是否尝试过比较两者之间的容忍度？我相信这两个MFCC是浮点数的数组，并且测试精确相等性可能不是明智的。尝试使用具有一定公差的app.module.ts，然后确定公差是否足够好。

尽管如此，我仍然想念您说形状不匹配，而且我对bob.ap并没有自信地对此发表评论的经验。但是，由于窗口化的原因，经常会有一些库在输入数组的开头或结尾用零填充输入，并且如果其中之一做的不同，可能是造成这种情况的原因。

Answer 2

但是，两者的输出不同，甚至形状也不相同。正常吗？

是的，算法种类繁多，每种实现都选择自己的风格

还是我缺少一个参数？

这不仅与参数有关，在算法上也有差异，例如窗口形状（汉明与汉宁），梅尔过滤器的形状，梅尔过滤器的开始，梅尔过滤器的规格化，提升，dct风味等等。

如果要获得相同的结果，只需使用单个库进行提取，就很难同步它们。

为什么MFCC提取库返回不同的值？

2 个答案: