{"data":[{"source":"bio.acousti.ca","id":"16462","type":"article","title":"Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms","author":"Esfahanian, Mahdi; Erdol, Nurgun; Gerstein, Edmund; Zhuang, Hanqi","editor":null,"year":"2017","month":null,"journal":"Applied Acoustics","booktitle":null,"series":null,"howpublished":null,"volume":"120","number":null,"pages":"158 - 166","chapter":null,"edition":null,"publisher":null,"organization":null,"institution":null,"school":null,"address":null,"type_of_work":null,"note":null,"isbn":null,"issn":"0003682X","doi":"10.1016\/j.apacoust.2017.01.025","url":"http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0003682X17300774","attachments":null,"keywords":null,"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\u2013frequency 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\u2019s dataset reveals that LBP features improved performance accuracy up to 43% over time\u2013frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.","type_name":"Journal Article","journal_abbreviation":"Applied Acoustics","pmid":null,"info_url":"https:\/\/bio.acousti.ca\/node\/16462"}]}