  Automatic analysis of bioacoustic signals is a fundamental tool to evaluate
the vitality of our planet. Frogs and bees, for instance, may act like
biological sensors providing information about environmental changes. This task
is fundamental for ecological monitoring still includes many challenges such as
nonuniform signal length processing, degraded target signal due to
environmental noise, and the scarcity of the labeled samples for training
machine learning. To tackle these challenges, we present a bioacoustic signal
classifier equipped with a discriminative mechanism to extract useful features
for analysis and classification efficiently. The proposed classifier does not
require a large amount of training data and handles nonuniform signal length
natively. Unlike current bioacoustic recognition methods, which are
task-oriented, the proposed model relies on transforming the input signals into
vector subspaces generated by applying Singular Spectrum Analysis (SSA). Then,
a subspace is designed to expose discriminative features. The proposed model
shares end-to-end capabilities, which is desirable in modern machine learning
systems. This formulation provides a segmentation-free and noise-tolerant
approach to represent and classify bioacoustic signals and a highly compact
signal descriptor inherited from SSA. The validity of the proposed method is
verified using three challenging bioacoustic datasets containing anuran, bee,
and mosquito species. Experimental results on three bioacoustic datasets have
shown the competitive performance of the proposed method compared to commonly
employed methods for bioacoustics signal classification in terms of accuracy.
