librosa/feature/utils.py
def delta(data, width=9, order=1, axis=-1, mode='interp', **kwargs):
    r'''Compute delta features: local estimate of the derivative
    of the input data along the selected axis.

    Delta features are computed Savitsky-Golay filtering.

    Parameters
    ----------
    data      : np.ndarray
        the input data matrix (eg, spectrogram)

    width     : int, positive, odd [scalar]
        Number of frames over which to compute the delta features.
        Cannot exceed the length of `data` along the specified axis.
        If `mode='interp'`, then `width` must be at least `data.shape[axis]`.

    order     : int > 0 [scalar]
        the order of the difference operator.
        1 for first derivative, 2 for second, etc.

    axis      : int [scalar]
        the axis along which to compute deltas.
        Default is -1 (columns).

    mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'}
        Padding mode for estimating differences at the boundaries.

    kwargs : additional keyword arguments
        See `scipy.signal.savgol_filter`

    Returns
    -------
    delta_data   : np.ndarray [shape=(d, t)]
        delta matrix of `data` at specified order

    Notes
    -----
    This function caches at level 40.

    See Also
    --------
    scipy.signal.savgol_filter

    Examples
    --------
    Compute MFCC deltas, delta-deltas

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> mfcc = librosa.feature.mfcc(y=y, sr=sr)
    >>> mfcc_delta = librosa.feature.delta(mfcc)
    >>> mfcc_delta
    array([[  1.666e+01,   1.666e+01, ...,   1.869e-15,   1.869e-15],
           [  1.784e+01,   1.784e+01, ...,   6.085e-31,   6.085e-31],
           ...,
           [  7.262e-01,   7.262e-01, ...,   9.259e-31,   9.259e-31],
           [  6.578e-01,   6.578e-01, ...,   7.597e-31,   7.597e-31]])

    >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
    >>> mfcc_delta2
    array([[ -1.703e+01,  -1.703e+01, ...,   3.834e-14,   3.834e-14],
           [ -1.108e+01,  -1.108e+01, ...,  -1.068e-30,  -1.068e-30],
           ...,
           [  4.075e-01,   4.075e-01, ...,  -1.565e-30,  -1.565e-30],
           [  1.676e-01,   1.676e-01, ...,  -2.104e-30,  -2.104e-30]])

    >>> import matplotlib.pyplot as plt
    >>> plt.subplot(3, 1, 1)
    >>> librosa.display.specshow(mfcc)
    >>> plt.title('MFCC')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 2)
    >>> librosa.display.specshow(mfcc_delta)
    >>> plt.title(r'MFCC-$\Delta$')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 3)
    >>> librosa.display.specshow(mfcc_delta2, x_axis='time')
    >>> plt.title(r'MFCC-$\Delta^2$')
    >>> plt.colorbar()
    >>> plt.tight_layout()

    '''

    data = np.atleast_1d(data)

    if mode == 'interp' and width > data.shape[axis]:
        raise ParameterError("when mode='interp', width={} "
                             "cannot exceed data.shape[axis]={}".format(width, data.shape[axis]))

    if width < 3 or np.mod(width, 2) != 1:
        raise ParameterError('width must be an odd integer >= 3')

    if order <= 0 or not isinstance(order, int):
        raise ParameterError('order must be a positive integer')

    kwargs.pop('deriv', None)
    kwargs.setdefault('polyorder', order)
    return scipy.signal.savgol_filter(data, width,
                                      deriv=order,
                                      axis=axis,
                                      mode=mode,
                                      **kwargs)
def delta(data, width=9, order=1, axis=-1, mode='interp', **kwargs):
    r'''Compute delta features: local estimate of the derivative
    of the input data along the selected axis.

    Delta features are computed Savitsky-Golay filtering.

    Parameters
    ----------
    data      : np.ndarray
        the input data matrix (eg, spectrogram)

    width     : int, positive, odd [scalar]
        Number of frames over which to compute the delta features.
        Cannot exceed the length of `data` along the specified axis.
        If `mode='interp'`, then `width` must be at least `data.shape[axis]`.

    order     : int > 0 [scalar]
        the order of the difference operator.
        1 for first derivative, 2 for second, etc.

    axis      : int [scalar]
        the axis along which to compute deltas.
        Default is -1 (columns).

    mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'}
        Padding mode for estimating differences at the boundaries.

    kwargs : additional keyword arguments
        See `scipy.signal.savgol_filter`

    Returns
    -------
    delta_data   : np.ndarray [shape=(d, t)]
        delta matrix of `data` at specified order

    Notes
    -----
    This function caches at level 40.

    See Also
    --------
    scipy.signal.savgol_filter

    Examples
    --------
    Compute MFCC deltas, delta-deltas

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> mfcc = librosa.feature.mfcc(y=y, sr=sr)
    >>> mfcc_delta = librosa.feature.delta(mfcc)
    >>> mfcc_delta
    array([[  1.666e+01,   1.666e+01, ...,   1.869e-15,   1.869e-15],
           [  1.784e+01,   1.784e+01, ...,   6.085e-31,   6.085e-31],
           ...,
           [  7.262e-01,   7.262e-01, ...,   9.259e-31,   9.259e-31],
           [  6.578e-01,   6.578e-01, ...,   7.597e-31,   7.597e-31]])

    >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
    >>> mfcc_delta2
    array([[ -1.703e+01,  -1.703e+01, ...,   3.834e-14,   3.834e-14],
           [ -1.108e+01,  -1.108e+01, ...,  -1.068e-30,  -1.068e-30],
           ...,
           [  4.075e-01,   4.075e-01, ...,  -1.565e-30,  -1.565e-30],
           [  1.676e-01,   1.676e-01, ...,  -2.104e-30,  -2.104e-30]])

    >>> import matplotlib.pyplot as plt
    >>> plt.subplot(3, 1, 1)
    >>> librosa.display.specshow(mfcc)
    >>> plt.title('MFCC')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 2)
    >>> librosa.display.specshow(mfcc_delta)
    >>> plt.title(r'MFCC-$\Delta$')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 3)
    >>> librosa.display.specshow(mfcc_delta2, x_axis='time')
    >>> plt.title(r'MFCC-$\Delta^2$')
    >>> plt.colorbar()
    >>> plt.tight_layout()

    '''

    data = np.atleast_1d(data)

    if mode == 'interp' and width > data.shape[axis]:
        raise ParameterError("when mode='interp', width={} "
                             "cannot exceed data.shape[axis]={}".format(width, data.shape[axis]))

    if width < 3 or np.mod(width, 2) != 1:
        raise ParameterError('width must be an odd integer >= 3')

    if order <= 0 or not isinstance(order, int):
        raise ParameterError('order must be a positive integer')

    kwargs.pop('deriv', None)
    kwargs.setdefault('polyorder', order)
    return scipy.signal.savgol_filter(data, width,
                                      deriv=order,
                                      axis=axis,
                                      mode=mode,
                                      **kwargs)
def delta ( data , width = 9 , order = 1 , axis = - 1 , mode = 'interp' , * * kwargs ) : data = np . atleast_1d ( data ) if mode == 'interp' and width > data . shape [ axis ] : raise ParameterError ( "when mode='interp', width={} " "cannot exceed data.shape[axis]={}" . format ( width , data . shape [ axis ] ) ) if width < 3 or np . mod ( width , 2 ) != 1 : raise ParameterError ( 'width must be an odd integer >= 3' ) if order <= 0 or not isinstance ( order , int ) : raise ParameterError ( 'order must be a positive integer' ) kwargs . pop ( 'deriv' , None ) kwargs . setdefault ( 'polyorder' , order ) return scipy . signal . savgol_filter ( data , width , deriv = order , axis = axis , mode = mode , * * kwargs )
r'''Compute delta features: local estimate of the derivative
    of the input data along the selected axis.

    Delta features are computed Savitsky-Golay filtering.

    Parameters
    ----------
    data      : np.ndarray
        the input data matrix (eg, spectrogram)

    width     : int, positive, odd [scalar]
        Number of frames over which to compute the delta features.
        Cannot exceed the length of `data` along the specified axis.
        If `mode='interp'`, then `width` must be at least `data.shape[axis]`.

    order     : int > 0 [scalar]
        the order of the difference operator.
        1 for first derivative, 2 for second, etc.

    axis      : int [scalar]
        the axis along which to compute deltas.
        Default is -1 (columns).

    mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'}
        Padding mode for estimating differences at the boundaries.

    kwargs : additional keyword arguments
        See `scipy.signal.savgol_filter`

    Returns
    -------
    delta_data   : np.ndarray [shape=(d, t)]
        delta matrix of `data` at specified order

    Notes
    -----
    This function caches at level 40.

    See Also
    --------
    scipy.signal.savgol_filter

    Examples
    --------
    Compute MFCC deltas, delta-deltas

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> mfcc = librosa.feature.mfcc(y=y, sr=sr)
    >>> mfcc_delta = librosa.feature.delta(mfcc)
    >>> mfcc_delta
    array([[  1.666e+01,   1.666e+01, ...,   1.869e-15,   1.869e-15],
           [  1.784e+01,   1.784e+01, ...,   6.085e-31,   6.085e-31],
           ...,
           [  7.262e-01,   7.262e-01, ...,   9.259e-31,   9.259e-31],
           [  6.578e-01,   6.578e-01, ...,   7.597e-31,   7.597e-31]])

    >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
    >>> mfcc_delta2
    array([[ -1.703e+01,  -1.703e+01, ...,   3.834e-14,   3.834e-14],
           [ -1.108e+01,  -1.108e+01, ...,  -1.068e-30,  -1.068e-30],
           ...,
           [  4.075e-01,   4.075e-01, ...,  -1.565e-30,  -1.565e-30],
           [  1.676e-01,   1.676e-01, ...,  -2.104e-30,  -2.104e-30]])

    >>> import matplotlib.pyplot as plt
    >>> plt.subplot(3, 1, 1)
    >>> librosa.display.specshow(mfcc)
    >>> plt.title('MFCC')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 2)
    >>> librosa.display.specshow(mfcc_delta)
    >>> plt.title(r'MFCC-$\Delta$')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 3)
    >>> librosa.display.specshow(mfcc_delta2, x_axis='time')
    >>> plt.title(r'MFCC-$\Delta^2$')
    >>> plt.colorbar()
    >>> plt.tight_layout()
r Compute delta features : local estimate of the derivative of the input data along the selected axis .