Automatically detecting animal signals in soundscape recordings is of benefit to passive acoustic monitoring programs which may be undertaken for research or conservation. Numerous algorithms exist, which are typically optimized for certain situations (i.e., certain animal sound types and ambient noise conditions). Adding to the library of algorithms, this paper developed, tested, and compared three detectors for Omura's whale vocalizations (15–62 Hz; <15 s) in marine soundscape recordings which contained noise from other animals, wind, earthquakes, ships, and seismic surveys. All three detectors were based on processing of spectrographic representations. The specific methods were spectrogram cross-correlation, entropy computation, and spectral intensity “blob” tracing. The latter two were general-purpose detectors that were adapted for detection of Omura's whale vocalizations. Detector complexity and post-processing effort varied across the three detectors. Performance was assessed qualitatively using demonstrative examples, and quantitatively using Receiver-Operating Characteristics and Precision-Recall curves. While the results of quantitative assessment were dominated by the spectrogram cross-correlation method, qualitative assessment showed that all three detectors offered promising performance.
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