Integrasi Bioakustik dan Sains Komputasi untuk Memetakan Komunikasi Mamalia Laut sebagai Upaya Konservasi Satwa Dilindungi di Samudera Tropis
DOI:
https://doi.org/10.59031/jnts.v1i3.777Keywords:
Bioacoustics, Marine Mammals, Computational Science, Artificial Intelligence, ConservationAbstract
This study aims to analyze the communication of marine mammals, especially whales and dolphins, through a bioacoustic approach combined with computational science as an effort to support conservation in the Tropical Ocean region. The focus of the location is on the Banda Sea, the Seram Sea, and the tropical Pacific region which are important migration routes for marine mammals. Data were obtained from underwater sound recordings using hydrophones, accompanied by visual observations to validate the behavior and existence of species. The analysis is carried out through several stages, including signal pre-processing with noise filtering and sound segmentation, spectral analysis using Fast Fourier Transform (FFT), as well as the creation of a spectrogram to visualize vocalization patterns. Machine learning algorithms such as Support Vector Machine (SVM) are used to classify interspecies voices, while deep learning approaches are applied to identify more complex communication patterns, including dialect variations. The results showed that whales produced low-frequency vocalizations (20–200 Hz) for long-distance communication, while dolphins used high-frequency clicks and whistles (5–20 kHz) for echolocation and social interaction. The integration of bioacoustics and artificial intelligence improves the accuracy of sound classification by more than 90%. These findings confirm the effectiveness of computational-based non-invasive methods in monitoring the presence and behavior of marine mammals and provide a scientific basis for sustainable conservation.
References
Andriolo, A., de Castro, F. R., Amorim, T., Miranda, G., Di Tullio, J., Moron, J., Ribeiro, B., Ramos, G., & Mendes, R. R. (2018). Marine mammal bioacoustics using towed array systems in the western South Atlantic Ocean. In Coastal Research Library (Vol. 22, pp. 113–147). Springer. https://doi.org/10.1007/978-3-319-56985-7_5
Azzolin, M., Gannier, A., Lammers, M. O., Oswald, J. N., Papale, E., Buscaino, G., Buffa, G., Mazzola, S., & Giacoma, C. (2014). Combining whistle acoustic parameters to discriminate Mediterranean odontocetes during passive acoustic monitoring. The Journal of the Acoustical Society of America, 135(1), 502–512. https://doi.org/10.1121/1.4845275
Caruso, F., Dong, L., Lin, M., Liu, M., Gong, Z., Xu, W., Alonge, G., & Li, S. (2020). Monitoring of a nearshore small dolphin species using passive acoustic platforms and supervised machine learning techniques. Frontiers in Marine Science, 7, 267. https://doi.org/10.3389/fmars.2020.00267
Casey, C., Reichmuth, C., Costa, D. P., & Le Boeuf, B. (2018). The rise and fall of dialects in northern elephant seals. Proceedings of the Royal Society B: Biological Sciences, 285(1892), 20182176. https://doi.org/10.1098/rspb.2018.2176
Changapur, M. W., Seema, S., & Sowmya, B. J. (2023). Bioacoustics monitoring to improve conservation efforts for endangered species. In 2023 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS) (pp. 1–6). IEEE. https://doi.org/10.1109/CSITSS60515.2023.10334168
Dalpaz, L., Paro, A. D., Daura-Jorge, F. G., Rossi-Santos, M., Norris, T. F., Ingram, S. N., & Wedekin, L. L. (2021). Better together: Analysis of integrated acoustic and visual methods when surveying a cetacean community. Marine Ecology Progress Series, 678, 197–209. https://doi.org/10.3354/meps13898
Dunlop, R. A. (2018). The communication space of humpback whale social sounds in wind-dominated noise. Journal of the Acoustical Society of America, 144(2), 540–551. https://doi.org/10.1121/1.5047744
Filatova, O. A., Deecke, V. B., Ford, J. K. B., Matkin, C. O., Barrett-Lennard, L. G., Guzeev, M. A., Burdin, A. M., & Hoyt, E. (2012). Call diversity in the North Pacific killer whale populations: Implications for dialect evolution and population history. Animal Behaviour, 83(3), 595–603. https://doi.org/10.1016/j.anbehav.2011.12.013
Fleming, A. H., Yack, T., Redfern, J. V., Becker, E. A., Moore, T. J., & Barlow, J. (2018). Combining acoustic and visual detections in habitat models of Dall’s porpoise. Ecological Modelling, 384, 198–208. https://doi.org/10.1016/j.ecolmodel.2018.06.014
Ford, J. K. B. (2017). Dialects. In B. Würsig, J. G. M. Thewissen, & K. M. Kovacs (Eds.), Encyclopedia of marine mammals (3rd ed., pp. 253–254). Academic Press. https://doi.org/10.1016/B978-0-12-804327-1.00104-7
Frankel, A. S., Ellison, W. T., Vigness-Raposa, K. J., Giard, J. L., & Southall, B. L. (2016). Stochastic modeling of behavioral response to anthropogenic sounds. In Advances in Experimental Medicine and Biology (Vol. 875, pp. 321–329). Springer. https://doi.org/10.1007/978-1-4939-2981-8_38
Frasier, K. E., Garrison, L. P., Soldevilla, M. S., Wiggins, S. M., & Hildebrand, J. A. (2021). Cetacean distribution models based on visual and passive acoustic data. Scientific Reports, 11(1), 8240. https://doi.org/10.1038/s41598-021-87577-1
Hastings, M. C., & Au, W. W. L. (2012). Marine bioacoustics and technology: The new world of marine acoustic ecology. AIP Conference Proceedings, 1495(1), 273–282. https://doi.org/10.1063/1.4765920
Kavanagh, A. S., Owen, K., Williamson, M. J., Blomberg, S. P., Noad, M. J., Goldizen, A. W., Kniest, E., Cato, D. H., & Dunlop, R. A. (2017). Evidence for the functions of surface-active behaviors in humpback whales (Megaptera novaeangliae). Marine Mammal Science, 33(1), 313–334. https://doi.org/10.1111/mms.12374
Kimura, S., Akamatsu, T., Wang, K., Wang, D., Li, S., Dong, S., & Arai, N. (2009). Comparison of stationary acoustic monitoring and visual observation of finless porpoises. Journal of the Acoustical Society of America, 125(1), 547–553. https://doi.org/10.1121/1.3021302
López, B. D., & Shirai, J. A. B. (2010). Mediterranean common bottlenose dolphin’s repertoire and communication use. In Dolphins: Anatomy, behavior and threats (pp. 129–147). Nova Science Publishers.
Nadir, M., Adnan, S. M., Aziz, S., & Khan, M. U. (2020). Marine mammals classification using acoustic binary patterns. Archives of Acoustics, 45(4), 721–731. https://doi.org/10.24425/aoa.2020.135278
Nanaware, S., Shastri, R., Joshi, Y., & Das, A. (2014). Passive acoustic detection and classification of marine mammal vocalizations. In 2014 International Conference on Communication and Signal Processing (ICCSP) (pp. 493–497). IEEE. https://doi.org/10.1109/ICCSP.2014.6949891
Nelms, S. E., Alfaro-Shigueto, J., Arnould, J. P. Y., Avila, I. C., Nash, S. B., Campbell, E., Carter, M. I. D., Collins, T., Currey, R. J. C., Domit, C., Franco-Trecu, V., Fuentes, M. M. P. B., … Godley, B. J. (2021). Marine mammal conservation: Over the horizon. Endangered Species Research, 44, 291–325. https://doi.org/10.3354/esr01115
Papale, E., Fanizza, C., Buscaino, G., Ceraulo, M., Cipriano, G., Crugliano, R., Grammauta, R., Gregorietti, M., Renò, V., Ricci, P., Santacesaria, F. C., Maglietta, R., & Carlucci, R. (2020). The social role of vocal complexity in striped dolphins. Frontiers in Marine Science, 7, 584301. https://doi.org/10.3389/fmars.2020.584301
Poupard, M., De Montgolfier, B., & Glotin, H. (2019). Ethoacoustic by Bayesian non-parametric and stochastic neighbor embedding to forecast anthropic pressure on dolphins. In OCEANS 2019 – Marseille (pp. 1–6). IEEE. https://doi.org/10.1109/OCEANSE.2019.8867126
Putland, R. L., Merchant, N. D., Farcas, A., & Radford, C. A. (2018). Vessel noise cuts down communication space for vocalizing fish and marine mammals. Global Change Biology, 24(4), 1708–1721. https://doi.org/10.1111/gcb.13996
Reckendorf, A., Seidelin, L., & Wahlberg, M. (2023). Marine mammal acoustics. In Marine mammals: A deep dive into the world of science (pp. 15–31). Springer. https://doi.org/10.1007/978-3-031-06836-2_2
Reynolds, J. E., III, Marsh, H., & Ragen, T. J. (2009). Marine mammal conservation. Endangered Species Research, 7(1), 23–28. https://doi.org/10.3354/esr00179
Romeu, B., Cantor, M., Bezamat, C., Simões-Lopes, P. C., & Daura-Jorge, F. G. (2017). Bottlenose dolphins that forage with artisanal fishermen whistle differently. Ethology, 123(12), 906–915. https://doi.org/10.1111/eth.12665
Schliep, E. M., Gelfand, A. E., Clark, C. W., Mayo, C. A., McKenna, B., Parks, S. E., Yack, T. M., & Schick, R. S. (2024). Assessing marine mammal abundance: A novel data fusion. Annals of Applied Statistics, 18(4), 3071–3090. https://doi.org/10.1214/24-AOAS1924
Stowell, D. (2017). Computational bioacoustic scene analysis. In Computational analysis of sound scenes and events (pp. 303–333). Springer. https://doi.org/10.1007/978-3-319-63450-0_11
Tyack, P. L., & Adamczak, S. K. (2019). Bioacoustics. In Encyclopedia of Ocean Sciences (3rd ed., Vols. 1–5, pp. V2-529–V2-535). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.11397-1
Van Cise, A. M., Mahaffy, S. D., Baird, R. W., Mooney, T. A., & Barlow, J. (2018). Song of my people: Dialect differences among sympatric social groups of short-finned pilot whales in Hawai’i. Behavioral Ecology and Sociobiology, 72(12), 193. https://doi.org/10.1007/s00265-018-2596-1
Vigas, V. P., Volpi, D., Alves, F. V., Silva, G. O., & Saraiva, E. F. (2020). An application of Hotelling’s T2 test for the comparison of the visual-acoustic method in the identification of ingestive cattle behavior. Revista Brasileira de Biometria, 38(1), 79–91. https://doi.org/10.28951/rbb.v38i1.431
Williamson, L. D., Brookes, K. L., Scott, B. E., Graham, I. M., & Thompson, P. M. (2017). Diurnal variation in harbour porpoise detection—Potential implications for management. Marine Ecology Progress Series, 570, 223–232. https://doi.org/10.3354/meps12118
Wright, A. J., & Cosentino, A. M. (2015). JNCC guidelines for minimising the risk of injury and disturbance to marine mammals from seismic surveys: We can do better. Marine Pollution Bulletin, 100(1), 231–239. https://doi.org/10.1016/j.marpolbul.2015.08.045
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of New Trends in Sciences

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.






