Classification of Bioacoustic Signals with Tangent Singular Spectrum Analysis

Abstract

Automatic classification of bioacoustic signals is an essential tool in biology for laborious tasks such as environmental monitoring in areas of difficult access. A working system applied in the field must be able to run on small scale machines and make reasonable predictions from a small sample of data. Recently, a method called Grassmann singular spectrum analysis (GSSA) was introduced as the latest development in a line of research where bioacoustic signals are represented by subspaces. While this paradigm is compact and introduces a straightforward discriminant analysis for classification, it is based on a Grassmann kernel, which approximates the Grassmann manifold by a reproducing Hilbert kernel space, thus depending on a choice of a dictionary and not being able to capture the signals complexity from a small class sample. In this paper, we propose a method named tangent singular spectrum analysis (TSSA), which continues to exploit the advantages of subspace representation but does not rely on approximating the Grassmann manifold by a low-dimensional kernel. We formulate a discriminant analysis on a tangent space to the data sample mean, using the extrinsic coordinates of the manifold. The validity of TSSA is demonstrated through experiments on the Amazon rainforest Anuran dataset.