Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy

TL;DR

This study presents a two-stage classification approach using local binary patterns and contour features to improve the detection accuracy of North Atlantic right whale upcalls from acoustic data.

Contribution

It introduces a novel two-stage strategy combining energy detection, contour, and texture features, notably LBP, for enhanced whale upcall detection accuracy.

Findings

LBP features improve detection accuracy by 3-4%.

Support Vector Machine achieves high detection rates.

Texture-based features outperform traditional time-frequency features.

Abstract

In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time-frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Experimental results with the data set provided by the Cornell University Bioacoustics Research Program reveal that classifiers show accuracy improvements of 3% to 4% when using LBP features over time-frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve high upcall detection rates with LBP features.