Applying automatic aural classification to cetacean vocalizations

Passive acoustic methods are widely used to detect and classify marine mammals; however, these passive sonar systems are often triggered by other transient sources, producing many false alarms. Additionally, to positively identify marine mammals, large volumes of data are collected that need to be processed by a trained analyst. To reduce acoustic analyst workload, an automatic detector can be implemented that produces many detections, which feed into an automatic classifier that significantly reduces the number of false detections. This requires development of a classifier capable of performing inter-species classification. A prototype aural classifier has been developed at Defence R&D Canada that uses perceptual signal features which model the features employed by the human auditory system. Previous effort has shown the aural classifier successfully discriminated cetacean vocalizations from five species: North Atlantic right, humpback, bowhead, minke, and sperm whales. This paper examines the effects of replacing principal component analysis (PCA) with discriminant analysis (DA) for feature space dimensionality reduction. PCA projects data onto a lower dimensional space so as to preserve the greatest scatter of data points, whereas DA projects the data to achieve the greatest separation of classes. Benefits of implementing DA and improvements to classification results will be discussed.