Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours

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 patte...

Full description

Bibliographic Details
Published in:2020 International Joint Conference on Neural Networks (IJCNN)
Main Authors: Li, Pu, Liu, Xiaobai, Palmer, K. J., Fleishman, Erica, Gillespie, Douglas, Nosal, Eva Marie, Shiu, Yu, Klinck, Holger, Cholewiak, Danielle, Helble, Tyler, Roch, Marie A.
Format: Other Non-Article Part of Journal/Newspaper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:
acoustic
data synthesis
deep neural network
odontocetes
Whistle contour extraction
toothed whales
Online Access:https://risweb.st-andrews.ac.uk/portal/en/researchoutput/learning-deep-models-from-synthetic-data-for-extracting-dolphin-whistle-contours(6a04a536-b73b-44db-8fe2-9886b53aaba3).html
https://doi.org/10.1109/IJCNN48605.2020.9206992
http://www.scopus.com/inward/record.url?scp=85093866240&partnerID=8YFLogxK
Description
Summary: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.

Similar Items