@article {16462,
	title = {Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms},
	journal = {Applied Acoustics},
	volume = {120},
	year = {2017},
	month = {Jan-05-2017},
	pages = {158 - 166},
	abstract = {<p>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\&ndash;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\&rsquo;s dataset reveals that LBP features improved performance accuracy up to 43\% over time\&ndash;frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.</p>
},
	issn = {0003682X},
	doi = {10.1016/j.apacoust.2017.01.025},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774},
	author = {Esfahanian, Mahdi and Erdol, Nurgun and Gerstein, Edmund and Zhuang, Hanqi}
}
