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We present a learning-based method for extracting whistles of toothed whales(Odontoceti) in hydrophone recordings. Our method represents audio signals astime-frequency spectrograms and decomposes each spectrogram into a set oftime-frequency patches. A deep neural network learns archetypical patterns(e.g., crossings, frequency modulated sweeps) from the spectrogram patches andpredicts time-frequency peaks that are associated with whistles. We alsodeveloped a comprehensive method to synthesize training samples from backgroundenvironments and train the network with minimal human annotation effort. Weapplied the proposed learn-from-synthesis method to a subset of the publicDetection, Classification, Localization, and Density Estimation (DCLDE) 2011workshop data to extract whistle confidence maps, which we then processed withan existing contour extractor to produce whistle annotations. The F1-score ofour best synthesis method was 0.158 greater than our baseline whistleextraction algorithm (~25% improvement) when applied to common dolphin(Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.

Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours