Skip to contents

Compute the acoustic complexity index using scikit-maad

Source: R/scikit-maad-indices.R
maad_acoustic_complexity_index.Rd

ACI depends on the duration of the spectrogram as the derivation of the signal is normalized by the sum of the signal. Thus, if the background noise is high due to high acoustic activity the normalization by the sum of the signal reduced ACI. So ACI is low when there is no acoustic activity or high acoustic activity with continuous background noise. ACI is high only when acoustic activity is medium, with sounds well above the background noise.

Usage

maad_acoustic_complexity_index(object, maad = NULL)

Arguments

object

A Wave object or a spectrogram_maad object generated by maad_spectrogram. If a Wave-like object is provided, the spectrogram will be calculated using the default parameters.

maad

An optional maad object. If not provided, one will be created using getMaad().

Value

List comprising:

ACI_xx

Acoustic Complexity Index.

ACI_per_bin

Acoustic Complexity Index.

ACI_sum

Sum of ACI value per frequency bin (Common definition)

Details

For addition documentation see https://scikit-maad.github.io/generated/maad.features.temporal_acoustic_complexity_index.html pieretti2011sonicscrewdriver.

References