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1. Humpback whale song vocalization behavior and temporo-spatial distributions in the Norwegian and Barents Sea observed with a coherent hydrophone array

   https://doi.org/10.1121/10.0027254  

   Samantray, Saunak; Ghosh, Arpita; Seri, Sai Geetha; Mohebbi-Kalkhoran, Hamed; Godoe, Olav R; Makris, Nicholas C; Ahonen, Heidi; Makris, Purnima R (March 2024, The Journal of the Acoustical Society of America)

   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. 

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2. Classifying Humpback Whale Calls to Song and Non-Song Vocalizations using Bag of Words Descriptor on Acoustic Data

   https://doi.org/10.1109/ICMLA.2019.00150  

   Mohebbi-Kalkhoran, Hamed; Zhu, Chenyang; Schinault, Matthew; Ratilal, Purnima (December 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA))

   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. 

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3. Distributed Acoustic Sensing for Whale Vocalization Monitoring: A Vertical Deployment Field Test

   https://doi.org/10.1785/0220240389  

   Saw, Jaewon; Luo, Linqing; Chu, Kristy; Ryan, John; Soga, Kenichi; Wu, Yuxin (February 2025, Seismological Research Letters)

   Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments. 

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4. Audible changes in marine trophic ecology: Baleen whale song tracks foraging conditions in the eastern North Pacific

   https://doi.org/10.1371/journal.pone.0318624  

   Ryan, John P; Oestreich, William K; Benoit-Bird, Kelly J; Waluk, Chad M; Rueda, Carlos A; Cline, Danelle E; Zhang, Yanwu; Cheeseman, Ted; Calambokidis, John; Fahlbusch, James A; et al (February 2025, PLOS ONE)

   Halliday, William David (Ed.)

   Among tremendous biodiversity within the California Current Ecosystem (CCE) are gigantic mysticetes (baleen whales) that produce structured sequences of sound described as song. From six years of passive acoustic monitoring within the central CCE we measured seasonal and interannual variations in the occurrence of blue (Balaenoptera musculus), fin (Balaenoptera physalus), and humpback (Megaptera novaeangliae) whale song. Song detection during 11 months of the year defines its prevalence in this foraging habitat and its potential use in behavioral ecology research. Large interannual changes in song occurrence within and between species motivates examination of causality. Humpback whales uniquely exhibited continuous interannual increases, rising from 34% to 76% of days over six years, and we examine multiple hypotheses to explain this exceptional trend. Potential influences of physical factors on detectability – including masking and acoustic propagation – were not supported by analysis of wind data or modeling of acoustic transmission loss. Potential influences of changes in local population abundance, site fidelity, or migration timing were supported for two of the interannual increases in song detection, based on extensive local photo ID data (17,356 IDs of 2,407 individuals). Potential influences of changes in foraging ecology and efficiency were supported across all years by analyses of the abundance and composition of forage species. Following detrimental food web impacts of a major marine heatwave that peaked during the first year of the study, foraging conditions consistently improved for humpback whales in the context of their exceptional prey-switching capacity. Stable isotope data from humpback and blue whale biopsy samples are consistent with observed interannual variations in the regional abundance and composition of forage species. This study thus indicates that major interannual changes in detection of baleen whale song may reflect underlying variations in forage species availability driven by energetic variations in ecosystem state. 

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5. Rapid 1024-pixel Electrochemical Imaging at 10,000 Frames per Second using Monolithic CMOS Sensor and Multifunctional Data Acquisition System

   https://doi.org/10.1109/JSEN.2018.2835829  

   White, Kevin A.; Mulberry, Geoffrey; Kim, Brian N. (May 2018, IEEE Sensors Journal)

   Fast electrochemical imaging enables the dynamic study of electroactive molecule diffusion in neurotransmitter release from single cells and dopamine mapping in brain slices. In this paper, we discuss the design of an electrochemical imaging sensor using a monolithic CMOS sensor array and a multifunctional data acquisition system. Using post-CMOS fabrication, the CMOS sensor integrates 1024 on-chip electrodes on the surface and contains 1024 low-noise amplifiers to simultaneous process parallel electrochemical recordings. Each electrochemical electrode and amplifier are optimized to operate at 10.38 kHz sampling rate. To support the operation of the high-throughput CMOS device, a multifunctional data acquisition device is developed to provide the required speed and accuracy. The high analog data rate of 10.63 MHz from all 1024 amplifiers is redundantly sampled by the custom-designed data acquisition system which can process up to 73.6 MHz with up to ~400 Mbytes/s data rate to a computer using USB 3.0 interface. To contain the liquid above the electrochemical sensors and prevent electronic and wire damage, we packaged the monolithic sensor using a 3D-printed well. Using the presented device, 32 pixel × 32 pixel electrochemical imaging of dopamine diffusion is successfully demonstrated at over 10,000 frames per second, the fastest reported to date. 

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