Soundscape segregation based on visual analysis and discriminating features

Abstract

The distinction of landscapes based on their sound patterns is useful for several analyses. For instance, comparisons of audio files from different periods enable the detection of changes over time in a particular habitat, signaling events of importance, such as modifications in the balance between species and presence of new ones. The handling of a large number of different sound recordings in wild environments also reduces the set of sounds to be examined. However, the current efforts towards soundscape interpretation do not provide enough elements for researchers to automatically split soundscape datasets with degrees of similarity, thus requiring users' feedback for the grouping of highly related recordings. This work introduces a strategy for the exploration and analysis of soundscapes that highlights data characteristics related to differences and similarities among distinct soundscapes. It is based on a visual and numerical evaluation of feature spaces and was applied to three feature sets, namely acoustic indices and measurements, images from audio spectrograms depicted by classic features, and the same images depicted by features automatically generated by Deep Learning techniques. The results indicate that certain combinations of acoustic indices and measurements perform well for the discrimination task, although other feature sets have not been discarded. In addition, visual techniques were able to assist this type of analysis.