Caution

You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.10.2.

librosa.zero_crossings

librosa.zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True, zero_pos=True, axis=-1)[source]

Find the zero-crossings of a signal y: indices i such that sign(y[i]) != sign(y[j]).

If y is multi-dimensional, then zero-crossings are computed along the specified axis.

Parameters:

ynp.ndarray

The input array

thresholdfloat > 0 or None

If specified, values where -threshold <= y <= threshold are clipped to 0.

ref_magnitudefloat > 0 or callable

If numeric, the threshold is scaled relative to ref_magnitude.

If callable, the threshold is scaled relative to ref_magnitude(np.abs(y)).

padboolean

If True, then y[0] is considered a valid zero-crossing.

zero_posboolean

If True then the value 0 is interpreted as having positive sign.

If False, then 0, -1, and +1 all have distinct signs.

axisint

Axis along which to compute zero-crossings.

Returns:

zero_crossingsnp.ndarray [shape=y.shape, dtype=boolean]: Indicator array of zero-crossings in y along the selected axis.

Notes

This function caches at level 20.

Examples

>>> # Generate a time-series
>>> y = np.sin(np.linspace(0, 4 * 2 * np.pi, 20))
>>> y
array([  0.000e+00,   9.694e-01,   4.759e-01,  -7.357e-01,
        -8.372e-01,   3.247e-01,   9.966e-01,   1.646e-01,
        -9.158e-01,  -6.142e-01,   6.142e-01,   9.158e-01,
        -1.646e-01,  -9.966e-01,  -3.247e-01,   8.372e-01,
         7.357e-01,  -4.759e-01,  -9.694e-01,  -9.797e-16])
>>> # Compute zero-crossings
>>> z = librosa.zero_crossings(y)
>>> z
array([ True, False, False,  True, False,  True, False, False,
        True, False,  True, False,  True, False, False,  True,
       False,  True, False,  True], dtype=bool)

>>> # Stack y against the zero-crossing indicator
>>> librosa.util.stack([y, z], axis=-1)
array([[  0.000e+00,   1.000e+00],
       [  9.694e-01,   0.000e+00],
       [  4.759e-01,   0.000e+00],
       [ -7.357e-01,   1.000e+00],
       [ -8.372e-01,   0.000e+00],
       [  3.247e-01,   1.000e+00],
       [  9.966e-01,   0.000e+00],
       [  1.646e-01,   0.000e+00],
       [ -9.158e-01,   1.000e+00],
       [ -6.142e-01,   0.000e+00],
       [  6.142e-01,   1.000e+00],
       [  9.158e-01,   0.000e+00],
       [ -1.646e-01,   1.000e+00],
       [ -9.966e-01,   0.000e+00],
       [ -3.247e-01,   0.000e+00],
       [  8.372e-01,   1.000e+00],
       [  7.357e-01,   0.000e+00],
       [ -4.759e-01,   1.000e+00],
       [ -9.694e-01,   0.000e+00],
       [ -9.797e-16,   1.000e+00]])

>>> # Find the indices of zero-crossings
>>> np.nonzero(z)
(array([ 0,  3,  5,  8, 10, 12, 15, 17, 19]),)