Silbido profundo :an open source package for the use of deep learning to detect odontocete whistles

• Published in: The Journal of the Acoustical Society of America
• Main Authors: Conant, Peter C., Li, Pu, Liu, Xiaobai, Klinck, Holger, Fleishman, Erica, Gillespie, Douglas, Nosal, Eva-Marie, Roch, Marie A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Acoustics and ultrasonics; Pacific; Silbido; toothed whale
• Online Access: https://research-portal.st-andrews.ac.uk/en/researchoutput/silbido-profundo(de0d03ea-698f-4aa3-8a87-3858658c1220).html; https://doi.org/10.1121/10.0016631; https://research-repository.st-andrews.ac.uk/bitstream/10023/26799/1/Conant_2022_JSA_Silbido_profundo_CC.pdf

This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). P roceedings of the International Joint Conference on Neural Networks , July 19–24, Glasgow, Scotland, p. 10] is incorporated into silbido , an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.