Archives of Acoustics, 45, 4, pp. 721–731, 2020

Marine Mammals Classification using Acoustic Binary Patterns

Maheen NADIR
University of Engineering and Technology

Syed Muhammad ADNAN
University of Engineering and Technology

Sumair AZIZ
University of Engineering and Technology

Muhammad Umar KHAN
University of Engineering and Technology

Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70–30% training testing and 89.6% on 5-fold cross-validation experiments.
Keywords: marine mammals; 1D Local Binary Patterns; Mel frequency cepstral coefficients; feature extraction; passive acoustic monitoring
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DOI: 10.24425/aoa.2020.135278