
import bob.bio.spear.extractor

extractor = bob.bio.spear.extractor.CepstralExtended(
    # the parameters emulate the ones in the paper "A Comparison of Features for Synthetic Speech Detection" by
    # Md Sahidullah, Tomi Kinnunen, Cemal Hanilci

    pre_emphasis_coef=0.97, # as per the algorithm implemented in the paper
    n_ceps=20, # number of RFCC coefficients should be 20 - this is the number we cut off from whole Cepstrum
    n_filters=20, # number of filters in the bank is also 20
    # win_length_ms=32.,  # 32 ms - this insures the window size is 512 and also FFT size is 512
    # win_shift_ms=16.,  # This is the overlap - half of the window
    win_length_ms=20.,  # 20 ms - this is the value in the paper
    win_shift_ms=10.,  # This is the overlap - half of the window
    f_max=8000,  # this number insures we take half of the frequencies after FFT - so we retain only 257 values for 512 window
    mel_scale=False,
    rect_filter=True, # these features are computed with a rectangular filter, hence the name RFCC
    with_delta=True,  # As reported in the paper
    with_delta_delta=True,  # As reported in the paper
    energy_filter =True, # The paper uses power of FFT magnitude
    dct_norm = True, # The paper uses normed DCT-II variant
    delta_win=1, # the paper computes deltas on window of size 1
)

