我最近做了关于 MFCC 的功课，但我无法弄清楚使用这些库之间的一些区别。
我使用的 3 个库是：
samplerate = 16000
NFFT = 512
NCEPT = 13
第一部分：梅尔滤波器组
temp1_fb = pyspeech.get_filterbanks(nfilt=NFILT, nfft=NFFT, samplerate=sample1)
# speechpy do not divide 2 and add 1 when initializing
temp2_fb = speechpy.feature.filterbanks(num_filter=NFILT, fftpoints=NFFT, sampling_freq=sample1)
temp3_fb = librosa.filters.mel(sr=sample1, n_fft=NFFT, n_mels=NFILT)
# fix librosa normalized version
temp3_fb /= np.max(temp3_fb, axis=-1)[:, None]
只有 Speechpy 中的形状会得到 (, 512)，其他的都是 (, 257)。librosa的身材有点变形。
第二部分：MFCC
# pyspeech without lifter. Using hamming
temp1_mfcc = pyspeech.mfcc(speaker1, samplerate=sample1, winlen=0.025, winstep=0.01, numcep=NCEPT, nfilt=NFILT, nfft=NFFT,
preemph=0.97, ceplifter=0, winfunc=np.hamming, appendEnergy=False)
# speechpy need pre-emphasized. Using rectangular window fixed. Mel filter bank is not the same
temp2_mfcc = speechpy.feature.mfcc(emphasized_speaker1, sampling_frequency=sample1, frame_length=0.025, frame_stride=0.01,
num_cepstral=NCEPT, num_filters=NFILT, fft_length=NFFT)
# librosa need pre-emphasized. Using log energy. Its STFT using hanning, but its framing is not the same
temp3_energy = librosa.feature.melspectrogram(emphasized_speaker1, sr=sample1, S=temp3_pow.T, n_fft=NFFT,
hop_length=frame_step, n_mels=NFILT).T
temp3_energy = np.log(temp3_energy)
temp3_mfcc = librosa.feature.mfcc(emphasized_speaker1, sr=sample1, S=temp3_energy.T, n_mfcc=13, dct_type=2, n_fft=NFFT,
hop_length=frame_step).T
我已经尽力设定条件公平。Speechpy的图形变得更暗。
第三部分：Delta系数
temp1 = pyspeech.delta(mfcc_speaker1, 2)
temp2 = speechpy.processing.derivative_extraction(mfcc_speaker1.T, 1).T
# librosa along the frame axis
temp3 = librosa.feature.delta(mfcc_speaker1, width=5, axis=0, order=1)
我不能直接将mfcc设置为speechpy中的参数，否则会很奇怪。而这些参数最初的作用与我的预期不一样。
我想知道是什么因素造成了这些差异。只是我上面提到的东西吗？还是我犯了一些错误？希望详细点，谢谢。