maad.features.acoustic_richness_index

maad.features.acoustic_richness_index(Ht_array, M_array)[source]

Compute the acoustic richness index of an audio file.

This acoustic index was first described in [1]. The present version was ported from the R package Seewave [2].

Parameters:

Ht_array1d ndarray of floats: Vector containing the temporal entropy Ht of the selected files
M_array: 1d ndarray of floats: Vector containing the amplitude index M of the selected files

Returns:

AR1d ndarray of floats: Vector of acoustic richenss index

References

[1]

Depraetere, M., Pavoine, S., Jiguet, F., Gasc, A., Duvail, S., & Sueur, J. (2012). Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland. Ecological Indicators, 13, 46–54. DOI: 10.1016/j.ecolind.2011.05.006

[2]

Seewave : http://rug.mnhn.fr/seewave. Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics, 18(2), 213-226. DOI: 10.1080/09524622.2008.9753600

Examples

>>> import maad

>>> s, fs = maad.sound.load('../data/indices/S4A03895_20190522_060000.wav')
>>> Ht_6h00 = maad.features.temporal_entropy(s)
>>> M_6h00 = maad.features.temporal_median(s)

>>> s, fs = maad.sound.load('../data/indices/S4A03895_20190522_080000.wav')
>>> Ht_8h00= maad.features.temporal_entropy(s)
>>> M_8h00 = maad.features.temporal_median(s)

>>> s, fs = maad.sound.load('../data/indices/S4A03895_20190522_100000.wav')
>>> Ht_10h00 = maad.features.temporal_entropy(s)
>>> M_10h00 = maad.features.temporal_median(s)

>>> result = maad.features.acoustic_richness_index([Ht_6h00,Ht_8h00,Ht_10h00],[M_6h00,M_8h00,M_10h00]) 
>>> print(result) 
[0.11111111 0.44444444 1.        ]