A robust method to automatically detect fin whale acoustic presence in large and diverse passive acoustic datasets
Schall, E.; Parcerisas, C. (2022). A robust method to automatically detect fin whale acoustic presence in large and diverse passive acoustic datasets. J. Mar. Sci. Eng. 10(12): 1831. https://dx.doi.org/10.3390/jmse10121831
In: Journal of Marine Science and Engineering. MDPI: Basel. ISSN 2077-1312; e-ISSN 2077-1312, more
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| Keywords |
Balaenoptera physalus (Linnaeus, 1758) [WoRMS]
Marine/Coastal |
| Author keywords |
fin whale; Balaenoptera physalus; automatic detection; chorus; 20 Hz pulse; kurtosis |
| Project | Top | Authors |
- Marine Soundscapes in Shallow Water: Automated Tools for Characterization and Analysis, more
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| Authors | | Top |
- Schall, E.
- Parcerisas, C., more
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| Abstract |
The growing availability of long-term and large-scale passive acoustic recordings open the possibility of monitoring the vocal activity of elusive oceanic species, such as fin whales (Balaenoptera physalus), in order to acquire knowledge on their distribution, behavior, population structure and abundance. Fin whales produce low-frequency and high-intensity pulses, both as single vocalizations and as song sequences (only males) which can be detected over large distances. Numerous distant fin whales producing these pulses generate a so-called chorus, by spectrally and temporally overlapping single vocalizations. Both fin whale pulses and fin whale chorus provide a distinct source of information on fin whales present at different distances to the recording location. The manual review of vast amounts of passive acoustic data for the presence of single vocalizations and chorus by human experts is, however, time-consuming, often suffers from low reproducibility and in its entirety, it is practically impossible. In this publication, we present and compare robust algorithms for the automatic detection of fin whale choruses and pulses which yield good performance results (i.e., false positive rates < 3% and true positive rates > 76%) when applied to real-world passive acoustic datasets characterized by vast amounts of data, with only a small proportion of the data containing the target sounds, and diverse soundscapes from the Southern Ocean. |
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