Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition

by   Bernardo B. Gatto, et al.

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.



There are no comments yet.


page 1

page 12

page 15


End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network

In this paper, we present an end-to-end approach for environmental sound...

Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface

Physiological signals are often organized in the form of multiple dimens...

End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications

This paper presents end-to-end learning from spectrum data - an umbrella...

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios. We present sp...

EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

In recent years, real-time control of prosthetic hands has gained a grea...

EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models

Electromyogram (EMG) has been utilized to interface signals for prosthet...

Shape-based defect classification for Non Destructive Testing

The aim of this work is to classify the aerospace structure defects dete...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.