librosa.util.softmask¶
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librosa.util.softmask(X, X_ref, power=1, split_zeros=False)[source]¶ Robustly compute a softmask operation.
M = X**power / (X**power + X_ref**power)Parameters: - X : np.ndarray
The (non-negative) input array corresponding to the positive mask elements
- X_ref : np.ndarray
The (non-negative) array of reference or background elements. Must have the same shape as X.
- power : number > 0 or np.inf
If finite, returns the soft mask computed in a numerically stable way
If infinite, returns a hard (binary) mask equivalent to X > X_ref. Note: for hard masks, ties are always broken in favor of X_ref (mask=0).
- split_zeros : bool
If True, entries where X and X`_ref` are both small (close to 0) will receive mask values of 0.5.
Otherwise, the mask is set to 0 for these entries.
Returns: - mask : np.ndarray, shape=`X.shape`
The output mask array
Raises: - ParameterError
If X and X_ref have different shapes.
If X or X_ref are negative anywhere
If power <= 0
Examples
>>> X = 2 * np.ones((3, 3)) >>> X_ref = np.vander(np.arange(3.0)) >>> X array([[ 2., 2., 2.], [ 2., 2., 2.], [ 2., 2., 2.]]) >>> X_ref array([[ 0., 0., 1.], [ 1., 1., 1.], [ 4., 2., 1.]]) >>> librosa.util.softmask(X, X_ref, power=1) array([[ 1. , 1. , 0.667], [ 0.667, 0.667, 0.667], [ 0.333, 0.5 , 0.667]]) >>> librosa.util.softmask(X_ref, X, power=1) array([[ 0. , 0. , 0.333], [ 0.333, 0.333, 0.333], [ 0.667, 0.5 , 0.333]]) >>> librosa.util.softmask(X, X_ref, power=2) array([[ 1. , 1. , 0.8], [ 0.8, 0.8, 0.8], [ 0.2, 0.5, 0.8]]) >>> librosa.util.softmask(X, X_ref, power=4) array([[ 1. , 1. , 0.941], [ 0.941, 0.941, 0.941], [ 0.059, 0.5 , 0.941]]) >>> librosa.util.softmask(X, X_ref, power=100) array([[ 1.000e+00, 1.000e+00, 1.000e+00], [ 1.000e+00, 1.000e+00, 1.000e+00], [ 7.889e-31, 5.000e-01, 1.000e+00]]) >>> librosa.util.softmask(X, X_ref, power=np.inf) array([[ True, True, True], [ True, True, True], [False, False, True]], dtype=bool)