FindPorpoises: Deep learning for detection of harbor porpoise echolocation clicks

Abstract

Tom Norris expressed conservation concern for the vaquita, a close relative of harbor porpoises; here we report on a harbor porpoise (Phocoena phocoena) conservation effort. Tidal energy devices are installed in high-flow estuaries that are also prime harbor porpoise habitat. To study tidal energy device impacts on porpoises, sound was recorded in Minas Passage, Bay of Fundy, Canada. Analysis aimed to distinguish harbor porpoises from noise sources. “Click candidate” sounds were detected using the ratio between the harbor porpoise frequency band and lower guard band, then reviewed by humans to label which were correct. Because more “correct” instances were needed, data were augmented by mixing porpoise clicks with known noise, producing 20,000 “click present” labeled instances. Additionally, 20 000 “non-click” instances were extracted from noise recordings. Labeled instances were made into a 0.5-s equalized spectrograms for training deep-learning networks. Of the network architectures tried, the best was a convolutional neural network with pooling and fully connected layers, achieving 99.1% accuracy. User-friendly “FindPorpoises” software was made to pre-process raw files, detect candidate clicks, use the trained network to sort candidate clicks into correct and incorrect instances, and plot and tabulate the results by time of day, tide cycle, and month. [Work supported by OERA.]