2016/11/15 by Mahdi Esfahanian, Esfahanian, Mahdi, Hanqi Zhuang +5
Earth and Planetary Sciences · Environmental Science · #Arctic and Antarctic ice dynamics #FOS: Computer and information sciences #Marine animal studies overview #Sound (cs.SD) #Underwater Acoustics Research
paper · pdf · doi:10.48550/arxiv.1611.04947
openalex publication_date 2016/11/15 · openalex created_date 2016/11/30 · openalex updated_date 2026/07/28
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.