Unsupervised selection of acoustic indices: An experimental comparison for characterizing unlabeled audio recordings from sub-Andean forest soundscapes

Abstract

We present an experimental comparison of applying six unsupervised (i.e. not relying on class labels) and almost parameterless feature selection methods for ranking acoustic indices. The study is aimed at guiding the practitioner in choosing appropriate acoustic indices when, in absence of class labels, a small but meaningful number of features to characterize soundscapes is desired. Forty acoustic indices were considered, which correspond to seventeen temporal, spectral and soundscape features, along with their basic statistics. Three publicly available soundscape datasets, registered in a sub-Andean forest, were used for the experiments; moreover, several subsets were considered according to the different times of the day. Results reveal that the Acoustic Evenness Index is the most important one in terms of representational power according to the six considered selection criteria, followed by the Acoustic Complexity Index when conditions are relaxed to examine features ranked among the top-five. Besides, the Bioacoustic Index, the Acoustic Diversity Index and the Root Mean Square Energy stand out as important features when characterizing days in their separated parts.