A novel multi-class support vector machine classifier for automated classification of beaked whales and other small odontocetes

Abstract

Navy sonar has recently been implicated in several marine mammal stranding events. Beaked whales (particulary Mesoplodon densirostris) have been the predominant species involved in a number of these strandings. Monitoring and mitigating the effects of anthropogenic noise on marine mammals are active areas of research. Key to both monitoring and mitigation is the ability to automatically detect and classify animals, especially beaked whales. This paper presents a novel support vector machine based methodology for automated, species level classification of small odontocetes. The new classifier, called the class-specific support vector machine (CS-SVM), consists of multiple binary SVM's where each SVM discriminates between a class of interest and a common reference class. A main objective in the development of the CS-SVM was to realize a robust multi-class SVM whose implementation is simpler than existing multi-class SVM methods. A CS-SVM was trained to identify click vocalization from four species of odontocetes including Mesoplodon densirostris. The algorithm processes time series data in a fully automated fashion first detecting and then classifying click events. Results from the application of this automated classifier to the data sets provided by the 3^rd International Workshop on Detection and Classification of Marine Mammals Using Passive Acoustics are presented.