Grassmann Singular Spectrum Analysis for Bioacoustics Classification

Abstract

Bioacoustic signal classification is a powerful tool for biologists, assisting in tasks such as environmental monitoring of biomes in areas of difficult access, and providing clues about the evolution and categorization of animals from the perspective of similarity of their bioacoustic mechanisms. The recently proposed mutual singular spectrum analysis (MSSA) introduced a novel bioacoustic signal representation based on subspaces, which is compact and requires no cost intensive preprocessing techniques (e.g. segmentation, noise reduction or syllable extraction). However, MSSA has no discriminant mechanism to separate classes, and it assumes that a class is composed of linear combinations of the reference signals, which in practice is unlikely, and impairs study of the individuals' signals among the same species. In this paper we propose an extension named Grassmann singular spectrum analysis (GSSA), which preserves the advantages of MSSA in addition to the following contributions: we assume that a class may be composed of a set of subspaces and we simplify bioacoustic signal subspace representation by mapping the subspaces onto a Grassmann manifold; and we offer a discriminant mechanism to separate the species in a classification task. We demonstrate the validity of GSSA through a classification experiment on a publicly available bioacoustic signals dataset.