Automatic Detection and Visualization of Marine Mammal Sounds in British-Columbia, Canada.

Abstract

Planned developments of marine terminals along the British Columbia (B.C.) coast will lead to increased exposures of marine mammals to noise from vessel traffic. Monitoring marine mammals off B.C. is becoming increasingly important for assessing their interactions with anthropogenic activities. Over the last decade, a large number of autonomous and cabled hydrophones have been deployed in B.C. waters to detect marine mammal calls and to measure ambient noise levels. These systems collect large data volumes that require considerable time and effort to fully analyze and interpret. That often precludes a comprehensive analysis, and data are commonly archived before their full value is exploited. In this study we describe techniques to automatically detect, classify, and visualize sounds produced by several species of cetaceans that frequent B.C. waters. Blue and fin whale calls are detected using a spectrogram correlation approach. Killer whale, humpback whale and Pacific white sided dolphin vocalizations are detected by calculating the local variance of intensity in the spectrogram and then classified using a random forest classifier. A web visualization interface (PAMview) is used to easily navigate through, display, and share detection and classification results. This interface is organized as three interconnected visualization panels: 1) a geographic interface displays maps of the total number of detections for each species at all monitoring locations within an adjustable time period, 2) an interactive detection time series displays temporal variations of detections for several species at a given monitoring location, and 3) a multimedia panel allows the user to visualize spectrograms, listen to sounds and to validate automatic detections. A demonstration of the acoustic monitoring system developed will be performed using archival and real-time data from the VENUS and NEPTUNE ocean observatories.