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1. Source Level of Antarctic Blue and Fin Whale Sounds Recorded on Sonobuoys Deployed in the Deep-Ocean Off Antarctica

   https://doi.org/10.3389/fmars.2021.792651  

   Miller, Brian S. ; Calderan, Susannah ; Leaper, Russell ; Miller, Elanor J. ; Širović, Ana ; Stafford, Kathleen M. ; Bell, Elanor ; Double, Michael C. ( December 2021 , Frontiers in Marine Science)

   The source levels, SL, of Antarctic blue and fin whale calls were estimated using acoustic recordings collected from directional sonobuoys deployed during an Antarctic voyage in 2019. Antarctic blue whale call types included stereotyped song and downswept frequency-modulated calls, often, respectively, referred to as Z-calls (comprising song units-A, B, and C) and D-calls. Fin whale calls included 20 Hz pulses and 40 Hz downswept calls. Source levels were obtained by measuring received levels (RL) and modelling transmission losses (TL) for each detection. Estimates of SL were sensitive to the parameters used in TL models, particularly the seafloor geoacoustic properties and depth of the calling whale. For our best estimate of TL and whale-depth, mean SL in dB re 1 μPa ± 1 standard deviation ranged between 188–191 ± 6–8 dB for blue whale call types and 189–192 ± 6 dB for fin whale call types. These estimates of SL are the first from the Southern Hemisphere for D-calls and 40 Hz downsweeps, and the largest sample size to-date for Antarctic blue whale song. Knowledge of source levels is essential for estimating the detection range and communication space of these calls and will enable more accurate comparisons of detections of these sounds from sonobuoy surveys and across international long-term monitoring networks. 

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2. Shaken, not stirred: blue whales show no acoustic response to earthquake events

   https://doi.org/10.1098/rsos.220242  

   Barlow, Dawn R. ; Estrada Jorge, Mateo ; Klinck, Holger ; Torres, Leigh G. ( July 2022 , Royal Society Open Science)

   Quantifying how animals respond to disturbance events bears relevance for understanding consequences to population health. We investigate whether blue whales respond acoustically to naturally occurring episodic noise by examining calling before and after earthquakes (27 040 calls, 32 earthquakes; 27 January–29 June 2016). Two vocalization types were evaluated: New Zealand blue whale song and downswept vocalizations ('D calls'). Blue whales did not alter the number of D calls, D call received level or song intensity following earthquakes (paired t -tests, p > 0.7 for all). Linear models accounting for earthquake strength and proximity revealed significant relationships between change in calling activity surrounding earthquakes and prior calling activity (D calls: R 2 = 0.277, p < 0.0001; song: R 2 = 0.080, p = 0.028); however, these same relationships were true for ‘null’ periods without earthquakes (D calls: R 2 = 0.262, p < 0.0001; song: R 2 = 0.149, p = 0.0002), indicating that the pattern is driven by blue whale calling context regardless of earthquake presence. Our findings that blue whales do not respond to episodic natural noise provide context for interpreting documented acoustic responses to anthropogenic noise sources, including shipping traffic and petroleum development, indicating that they potentially evolved tolerance for natural noise sources but not novel noise from anthropogenic origins. 

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3. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

   https://doi.org/10.1111/gcb.15191  

   Davis, Genevieve_E ; Baumgartner, Mark_F ; Corkeron, Peter_J ; Bell, Joel ; Berchok, Catherine ; Bonnell, Julianne_M ; Bort_Thornton, Jacqueline ; Brault, Solange ; Buchanan, Gary_A ; Cholewiak, Danielle_M ; et al ( July 2020 , Global Change Biology)

   Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata) and North Atlantic right whales (NARW;Eubalaena glacialis). This study assesses the acoustic presence of humpback (Megaptera novaeangliae), sei (B. borealis), fin (B. physalus), and blue whales (B. musculus) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.

    

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4. Distributed acoustic sensing recordings of low-frequency whale calls and ship noise offshore Central Oregon

   https://doi.org/10.1121/10.0017104  

   Wilcock, William S. ; Abadi, Shima ; Lipovsky, Bradley P. ( February 2023 , JASA Express Letters)

   Distributed acoustic sensing (DAS) is a technique that measures strain changes along an optical fiber to distances of ∼100 km with a spatial sensitivity of tens of meters. In November 2021, 4 days of DAS data were collected on two cables of the Ocean Observatories Initiative Regional Cabled Array extending offshore central Oregon. Numerous 20 Hz fin whale calls, northeast Pacific blue whale A and B calls, and ship noises were recorded, highlighting the potential of DAS for monitoring the ocean. The data are publicly available to support studies to understand the sensitivity of submarine DAS for low-frequency acoustic monitoring.

    

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5. 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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