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Humpback whale breathing-related sounds were recorded on elements of a coherent hydrophone array subaperture deployed vertically at the Great South Channel on the US Northeastern continental shelf in Fall 2021, where half of the hydrophones were in-air and the rest submerged underwater. In-air hydrophones recorded breathing sounds with approximately 2.5 s duration, but smaller bandwidths compared to underwater hydrophones where signal energies extended beyond 50 kHz, and a mean underwater source level of 161 ± 4 dB re 1 μPa at 1 m, based on measurements at 22.9 m. The underwater recorded humpback whale breathing sound spectra displayed a broadband dip centered at 15.7 kHz, with approximately 400 Hz half-power bandwidth, likely caused by attenuation from propagation through pulsating air bubbles. The air bubble radius for natural frequency of oscillations at 15.7 kHz is estimated to be 0.205–0.21 mm. These bubbles are capable of removing energy from the forward propagated humpback breathing sounds via resonance absorption most pronounced at and near bubble natural oscillation frequency. Humpback whale distances from the vertically deployed hydrophones are estimated and tracked by matching the curved nonlinear travel-time wavefront of its breathing sounds, since the whale was in the near-field of the subarray. 

The vocalization behavior of humpback whales in the Norwegian and Barents Seas is examined based on recordings of a large-aperture, densely-populated coherent hydrophone array system. The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to provide detection, bearing-time estimation, time-frequency characterization and classification of the humpback whale vocalizations. The song vocalizations, composed of highly structured and repeatable set of phrases, were detected throughout the diel cycle between February 18 to March 8, 2014. The beamformed spectrograms of the detected humpback vocalizations are classified as song sequences based on inter-pulse intervals and time-frequency characteristics, verified by visual inspection. The song structure is compared for humpback whale vocalizations recorded at three distinct regions off the Norwegian coast, Alesund, Lofoten and Northern Finmark. Multiple bearing-time trajectories for humpback songs were simultaneously observed indicating multiple singers present at each measurement site. Humpback whale received call rates and temporo-spatial distributions are compared across the three measurement sites. Geographic mapping of humpback whale calls from their bearing-time trajectories is accomplished via the moving array triangulation technique. 

Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we develop automatic approaches for classifying humpback whale vocalizations into the two categories of song and non-song, employing machine learning techniques. The vocalization behavior of humpback whales was monitored over instantaneous vast areas of the Gulf of Maine using a large aperture coherent hydrophone array system via the passive ocean acoustic waveguide remote sensing technique over multiple diel cycles in Fall 2006. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification, showing effectiveness of the proposed approach for real-time classification at sea.