An End-to-End Workflow for Fin Whale Song Detection, Note Characterization, and Localization with Distributed Acoustic Sensing
2026-08-03 • Sound
Sound
AI summaryⓘ
The authors developed a complete method to detect and pinpoint fin whale sounds using special submarine fiber-optic cables that act like underwater microphones. Their method groups detected sounds into individual whale notes, analyzes their features, and estimates where the sounds came from. When tested on real whale songs, their approach was very accurate in finding and identifying whale notes. The authors also showed how their system could tell apart overlapping whale calls and track whale movement. This work helps turn complex underwater sound data into useful information for studying fin whales and could be used with other underwater sound devices.
Distributed Acoustic Sensing (DAS)Submarine fiber-optic cablesFin whale vocalizationsKurtosis-value pickerSpatio-temporal clusteringHyperbolic fittingSource localizationBioacousticsPrecision and recallAcoustic receiver arrays
Authors
Dídac Diego-Tortosa, Miriam Romagosa, Arantza Ugalde, Hugo Latorre, Sergi Ventosa, Jose Enrique García, Antonio Villaseñor
Abstract
Submarine fiber-optic cables instrumented with distributed acoustic sensing (DAS) provide an effective approach for large-scale monitoring of fin whales. We present an end-to-end workflow for detecting, characterizing, and localizing fin whale notes, tested on two submarine telecom cables in the Strait of Gibraltar and western Alboran Sea. The workflow applies a kurtosis-value picker adapted to narrow-band fin whale notes. Channel-wise detections are grouped into individual notes using density-based spatio-temporal clustering, cluster agglomeration, and hyperbolic fitting to reject incoherent picks. The retained clusters are characterized through temporal, spectral, and energy-related descriptors that support note-type discrimination and estimation of inter-note intervals. Relative arrival times across DAS channels are then used in a grid-search procedure to estimate candidate source locations. Evaluation against manually annotated detections from six fin whale songs yielded median pick-level precision of 0.990 and recall of 0.744, and median cluster-level precision of 0.880 and recall of 0.806. Representative applications demonstrate separation of overlapping vocalizations, characterization of type-A and type-B notes, and the inference of apparent source movement. By transforming dense DAS recordings into compact note-level bioacoustic information, the workflow provides an integrated framework for fin whale monitoring and a basis for adaptation to other synchronized acoustic receiver arrays.