def mfcc(y=None, sr=<NUM_LIT>, S=None, n_mfcc=<NUM_LIT:20>, dct_type=<NUM_LIT:2>, norm='<STR_LIT>', **kwargs):
if S is None:<EOL><INDENT>S = power_to_db(melspectrogram(y=y, sr=sr, **kwargs))<EOL><DEDENT>return scipy.fftpack.dct(S, axis=<NUM_LIT:0>, type=dct_type, norm=norm)[:n_mfcc]<EOL>
Mel-frequency cepstral coefficients (MFCCs)

    Parameters
    ----------
    y : np.ndarray [shape=(n,)] or None
        audio time series

    sr : number > 0 [scalar]
        sampling rate of `y`

    S : np.ndarray [shape=(d, t)] or None
        log-power Mel spectrogram

    n_mfcc: int > 0 [scalar]
        number of MFCCs to return

    dct_type : None, or {1, 2, 3}
        Discrete cosine transform (DCT) type.
        By default, DCT type-2 is used.

    norm : None or 'ortho'
        If `dct_type` is `2 or 3`, setting `norm='ortho'` uses an ortho-normal
        DCT basis.

        Normalization is not supported for `dct_type=1`.

    kwargs : additional keyword arguments
        Arguments to `melspectrogram`, if operating
        on time series input

    Returns
    -------
    M : np.ndarray [shape=(n_mfcc, t)]
        MFCC sequence

    See Also
    --------
    melspectrogram
    scipy.fftpack.dct

    Examples
    --------
    Generate mfccs from a time series

    >>> y, sr = librosa.load(librosa.util.example_audio_file(), offset=30, duration=5)
    >>> librosa.feature.mfcc(y=y, sr=sr)
    array([[ -5.229e+02,  -4.944e+02, ...,  -5.229e+02,  -5.229e+02],
           [  7.105e-15,   3.787e+01, ...,  -7.105e-15,  -7.105e-15],
           ...,
           [  1.066e-14,  -7.500e+00, ...,   1.421e-14,   1.421e-14],
           [  3.109e-14,  -5.058e+00, ...,   2.931e-14,   2.931e-14]])

    Use a pre-computed log-power Mel spectrogram

    >>> S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
    ...                                    fmax=8000)
    >>> librosa.feature.mfcc(S=librosa.power_to_db(S))
    array([[ -5.207e+02,  -4.898e+02, ...,  -5.207e+02,  -5.207e+02],
           [ -2.576e-14,   4.054e+01, ...,  -3.997e-14,  -3.997e-14],
           ...,
           [  7.105e-15,  -3.534e+00, ...,   0.000e+00,   0.000e+00],
           [  3.020e-14,  -2.613e+00, ...,   3.553e-14,   3.553e-14]])

    Get more components

    >>> mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)

    Visualize the MFCC series

    >>> import matplotlib.pyplot as plt
    >>> plt.figure(figsize=(10, 4))
    >>> librosa.display.specshow(mfccs, x_axis='time')
    >>> plt.colorbar()
    >>> plt.title('MFCC')
    >>> plt.tight_layout()

    Compare different DCT bases

    >>> m_slaney = librosa.feature.mfcc(y=y, sr=sr, dct_type=2)
    >>> m_htk = librosa.feature.mfcc(y=y, sr=sr, dct_type=3)
    >>> plt.figure(figsize=(10, 6))
    >>> plt.subplot(2, 1, 1)
    >>> librosa.display.specshow(m_slaney, x_axis='time')
    >>> plt.title('RASTAMAT / Auditory toolbox (dct_type=2)')
    >>> plt.colorbar()
    >>> plt.subplot(2, 1, 2)
    >>> librosa.display.specshow(m_htk, x_axis='time')
    >>> plt.title('HTK-style (dct_type=3)')
    >>> plt.colorbar()
    >>> plt.tight_layout()