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In this paper, we investigate the effectiveness of two-stage classificationstrategies in detecting north Atlantic right whale upcalls. Time-frequencymeasurements of data from passive acoustic monitoring devices are evaluated asimages. Vocalization spectrograms are preprocessed for noise reduction and toneremoval. First stage of the algorithm eliminates non-upcalls by an energydetection algorithm. In the second stage, two sets of features are extractedfrom the remaining signals using contour-based and texture based methods. Theformer is based on extraction of time-frequency features from upcall contours,and the latter employs a Local Binary Pattern operator to extractdistinguishing texture features of the upcalls. Subsequently evaluation phaseis carried out by using several classifiers to assess the effectiveness of boththe contour-based and texture-based features for upcall detection. Experimentalresults with the data set provided by the Cornell University BioacousticsResearch Program reveal that classifiers show accuracy improvements of 3% to 4%when using LBP features over time-frequency features. Classifiers such as theLinear Discriminant Analysis, Support Vector Machine, and TreeBagger achievehigh upcall detection rates with LBP features.

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