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