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

• Main Authors: Esfahanian, Mahdi, Zhuang, Hanqi, Erdol, Nurgun, Gerstein, Edmund
• Format: Report
• Language: unknown
• Published: arXiv 2016
• Subjects: Sound cs.SD; FOS Computer and information sciences; North Atlantic; North Atlantic right whale
• Online Access: https://dx.doi.org/10.48550/arxiv.1611.04947; https://arxiv.org/abs/1611.04947

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. : 32 pages, 11 figures, 4 tables