Mutual singular spectrum analysis for bioacoustics classification

Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose a novel bioacoustics signals classification method where no preprocessing techniques are involved and which is able to match sets of signals. The advantages of our proposed method include: a novel and compact representation for bioacoustics signals, which is independent of the signals length. In addition, no preprocessing is required, such as segmentation, noise reduction or syllable extraction. We show that our method is theoretically and practically attractive through experimental results employing a publicity available bioacoustics signal dataset.