Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms

Publication Type:Journal Article
Year of Publication:2017
Auteurs:Esfahanian, Erdol, Gerstein, Zhuang
Journal:Applied Acoustics
Volume:120
Pagination:158 - 166
Date Published:Jan-05-2017
ISSN:0003682X
Résumé:

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. Comparing ROC curves of machine learning algorithms obtained from Cornell University’s dataset reveals that LBP features improved performance accuracy up to 43% over time–frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.

URL:http://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774
DOI:10.1016/j.apacoust.2017.01.025
Short Title:Applied Acoustics
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Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith