maad.features.spectral_events

maad.features.spectral_events(Sxx_dB, dt, dB_threshold=6, rejectDuration=None, display=False, **kwargs)[source]

Compute acoustic events from a spectrogram [1] [2].

An acoustic event corresponds to the period of the signal above a threshold. An acoustic event could be short (at list one point if rejectDuration is None) or very long (the duration of the entire audio). Two acoustic events are separated by a period with low audio signal (ie below the threshold). Acoustic events are calculated frequency by frequency along time axis This function computes:

EVNspFraction : Fraction of events duration over total duration

EVNspmean : mean events duration (s)

EVNspcount : number of events per s

EVNsp : binary vector or matrix with 1 corresponding to event position

Parameters:

Sxx_dB2D array of floats: 2D in dB. Usually, better to work with spectrogram without stationnary noise in order to measure only acoustic activity above the background noise
dtfloat: time resolution in s (ie tn[1]-tn[0])
dB_thresholdscalar, optional, default is 6dB: data >Threshold is considered to be an event if the length is > rejectLength
rejectDurationscalar, optional, default is None: event shorter than rejectDuration are discarded duration is in s
displayboolean, optional, default is false: Display a plot with the number of events per s (EVNspCount) and a binary image with the detected events.
kwargsoptional. See matplotlib documentation

Returns:

EVNspFract :scalar: Fraction: events duration over total duration
EVNspMean: scalar: mean events duration in s
EVNspCount: scalar: number of events per s
EVNsp: ndarray of floats: binary matrix. 1 corresponds to event 0 corresponds to background

References

[1]

TOWSEY, Michael W. The calculation of acoustic indices derived from long-duration recordings of the natural environment. 2017. https://eprints.qut.edu.au/110634/1/QUTePrints110634_TechReport_Towsey2017August_AcousticIndices%20v3.pdf

[2]

QUT : https://github.com/QutEcoacoustics/audio-analysis. Michael Towsey, Anthony Truskinger, Mark Cottman-Fields, & Paul Roe. (2018, March 5). Ecoacoustics Audio Analysis Software v18.03.0.41 (Version v18.03.0.41). Zenodo. http://doi.org/10.5281/zenodo.1188744

Examples

>>> import maad
>>> import numpy as np
>>> s, fs = maad.sound.load('../data/cold_forest_daylight.wav')
>>> Sxx_power, tn, fn, ext = maad.sound.spectrogram (s, fs)  
>>> Sxx_noNoise= maad.sound.median_equalizer(Sxx_power) 
>>> Sxx_dB_noNoise = maad.util.power2dB(Sxx_noNoise)
>>> EVNspFract_per_bin, EVNspMean_per_bin, EVNspCount_per_bin, EVNsp = maad.features.spectral_events(Sxx_dB_noNoise, dt=tn[1]-tn[0], dB_threshold=6, rejectDuration=0.1, display=True, extent=ext)  
>>> print('Mean proportion of spectrogram with events: %2.2f%%' %np.mean(EVNspFract_per_bin)) 
Mean proportion of spectrogram with events: 0.01%