maad.features.all_temporal_features

maad.features.all_temporal_features(s, fs, nperseg=1024, roi=None, display=False, **kwargs)[source]

Compute all the temporal features for a signal.

Parameters:

s1D array: Input audio signal
fsfloat: Sampling frequency of audio signal
npersegint, optional: Length of segment to compute the FFT. The default is 1024.
roipandas.Series, optional: Region of interest where temporal features will be computed. Series must have a valid input format with index: min_t, min_f, max_t, max_f. The default is None.
kwargsadditional keyword arguments: If window=’hann’, additional keyword arguments to pass to sound.spectrum.

Returns:

temporal_featurespandas DataFrame: DataFrame with all temporal features computed in the spectrum

Examples

>>> from maad import features, sound
>>> s, fs = sound.load('../data/spinetail.wav')

Compute all the temporal features

>>> temporal_features = features.all_temporal_features(s,fs)
>>> print(temporal_features.iloc[0])
sm            -2.043264e-19
sv             1.167074e-03
ss            -6.547980e-03
sk             2.471161e+01
Time 5%        1.219048e+00
Time 25%       5.712109e+00
Time 50%       1.181896e+01
Time 75%       1.655583e+01
Time 95%       1.775166e+01
zcr            1.050040e+04
duration_50    1.001495e+01
duration_90    1.654441e+01
Name: 0, dtype: float64