maad.features.soundscape_index

maad.features.soundscape_index(Sxx_power, fn, flim_bioPh=(1000, 10000), flim_antroPh=(0, 1000), R_compatible='soundecology')[source]

Compute the Normalized Difference Soundscape Index from a power spectrogram [1].

Parameters:

Sxx_powerndarray of floats: 2d : Power Spectrogram
fnvector: frequency vector
flim_bioPhtupple (fmin, fmax), optional, default is (1000,10000): Frequency band of the biophony
flim_antroPh: tupple (fmin, fmax), optional, default is (0,1000): Frequency band of the anthropophony
R_compatiblestring, optional, default is “soundecology”: if ‘soundecology’, the result is similar to the package SoundEcology in R Otherwise, the result is specific to maad or Seewave R package

Returns:

NDSIscalar: (bioPh-antroPh)/(bioPh+antroPh)
ratioBAscalar: biophonic energy / anthropophonic energy
antroPhscalar: Acoustic energy in the anthropophonic bandwidth
bioPhscalar: Acoustic energy in the biophonic bandwidth

References

[1]

Kasten, Eric P., Stuart H. Gage, Jordan Fox, and Wooyeong Joo. 2012. The Remote Environmental Assessment Laboratory’s Acoustic Library: An Archive for Studying Soundscape Ecology. Ecological Informatics 12: 50-67. DOI: 10.1016/j.ecoinf.2012.08.001 https://doi.org/10.1016/j.ecoinf.2012.08.001

Ported from Seewave (http://rug.mnhn.fr/seewave) and soundecology R packages (cran.ms.unimelb.edu.au/web/packages/soundecology/soundecology.pdf).

Examples

>>> import maad
>>> s, fs = maad.sound.load('../data/cold_forest_daylight.wav')
>>> Sxx_power, tn, fn, ext = maad.sound.spectrogram (s, fs)  
>>> NDSI, ratioBA, antroPh, bioPh  = maad.features.soundscape_index(Sxx_power,fn)
>>> print('NDSI Soundecology : %2.2f ' %NDSI) 
NDSI Soundecology : 0.10
>>> NDSI, ratioBA, antroPh, bioPh  = maad.features.soundscape_index(Sxx_power,fn,R_compatible=None)
>>> print('NDSI MAAD: %2.2f ' %NDSI) 
NDSI MAAD: 0.99