我最近在做关于MFCC的作业，但是我无法弄清楚使用这些库之间的一些区别。
我使用的三个库分别是：
samplerate = 16000
NFFT = 512
NCEPT = 13
第一部分：Mel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)，其他全部为(, 257)。Librosa的图形稍微有些变形。
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
我已经尽力设置了公平的条件。比喻的形象变得更加深沉。
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作为speechpy的参数，否则会非常奇怪。而且这些参数原本的作用与我的预期不同。
我想知道是什么因素导致了这些差异。这只是我上面提到的问题吗？还是我犯了一些错误？希望得到详细说明，谢谢。