REFERENCES
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G.,
Varadarajan, B., & Vijayanarasimhan, S. (2016). YouTube-8M: a
large-scale video classification benchmark. Retrived from:
https://doi.org//10.48550/arXiv.1609.08675
Ainsworth, T. D., Hurd, C. L., Gates, R. D., & Boyd, P. W. (2020). How
do we overcome abrupt degradation of marine ecosystems and meet the
challenge of heat waves and climate extremes? Global Change
Biology , 26 (2), 343–354. https://doi.org/10.1111/gcb.14901
Allen, A. N., Harvey, M., Harrell, L., Jansen, A., Merkens, K. P., Wall,
C. C., Cattiau, J., & Oleson, E. M. (2021). A convolutional neural
network for automated detection of humpback whale song in a diverse,
long-term passive acoustic dataset. Frontiers in Marine Science ,8 . https://doi.org/10.3389/fmars.2021.607321
Almeira, J., & Guecha, S. (2019). Dominant power spectrums as a tool to
establish an ecoacoustic baseline in a premontane moist forest.Landscape and Ecological Engineering , 15 (1), 121–130.
https://doi.org/10.1007/s11355-018-0355-0
Kowarski, K.A., Wilson, C. C., Delarue, J. J. Y., Martin, S. B. (2021)..
Flemish Pass acoustic monitoring: ambient characterization and marine
mammal monitoring near a mobile offshore drilling unit. Technical report
by JASCO Applied Scienses for Wood Environment and Infrastructure
Solutions.
Bermant, P. C., Bronstein, M. M., Wood, R. J., Gero, S., & Gruber, D.
F. (2019). Deep machine learning techniques for the detection and
classification of sperm whale bioacoustics. Scientific Reports ,9 (1), 1–10. https://doi.org/10.1038/s41598-019-48909-4
Bittle, M., & Duncan, A. (2013). A review of current marine mammal
detection and classification algorithms for use in automated passive
acoustic monitoring. Proceedings of Acoustics, 2013, 1–8.
http://www.acoustics.asn.au/conference_proceedings/AAS2013/papers/p64.pdf
Bohnenstiehl, D. R., Lyon, R. P., Caretti, O. N., Ricci, S. W., &
Eggleston, D. B. (2018). Investigating the utility of ecoacoustic
metrics in marine soundscapes. Journal of Ecoacoustics ,2 (2), 1–1. https://doi.org/10.22261/JEA.R1156L
Bradfer-Lawrence, T., Gardner, N., Bunnefeld, L., Bunnefeld, N., Willis,
S. G., & Dent, D. H. (2019). Guidelines for the use of acoustic indices
in environmental research. Methods in Ecology and Evolution ,10 (10), 1796–1807. https://doi.org/10.1111/2041-210X.13254
Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010).
The balanced accuracy and its posterior distribution. 20th
International Conference on Pattern Recognition , 3121–3124.
https://doi.org/10.1109/ICPR.2010.764
Chinchor, N. (1992). MUC-4 evaluation metrics. Proceedings of the
4th Conference on Message Understanding - MUC4 ’92 , 22.
https://doi.org/10.3115/1072064.1072067
Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep
learning in ecology. Methods in Ecology and Evolution , 10(10) pp.
1632–1644). https://doi.org/10.1111/2041-210X.13256
Clink, D. J., & Klinck, H. (2020). Unsupervised acoustic classification
of individual gibbon females and the implications for passive acoustic
monitoring. Methods in Ecology and Evolution ,12(2), 328-341.
https://doi.org/10.1111/2041-210X.13520
Dimoff, S. A., Halliday, W. D., Pine, M. K., Tietjen, K. L., Juanes, F.,
& Baum, J. K. (2021). The utility of different acoustic indicators to
describe biological sounds of a coral reef soundscape. Ecological
Indicators , 124 . https://doi.org/10.1016/j.ecolind.2021.107435
Dong, X., Yan, N., & Wei, Y. (2018). Insect sound recognition based on
convolutional neural network. 2018 IEEE 3rd International
Conference on Image, Vision and Computing (ICIVC) , 855–859.
https://doi.org/10.1109/ICIVC.2018.8492871
Durette-Morin, D., Davies, K. T. A., Johnson, H. D., Brown, M. W.,
Moors-Murphy, H., Martin, B., & Taggart, C. T. (2019). Passive acoustic
monitoring predicts daily variation in North Atlantic right whale
presence and relative abundance in Roseway Basin, Canada. Marine
Mammal Science , 35 (4), 1280–1303.
https://doi.org/10.1111/mms.12602
Eftestøl, S., Flydal, K., Tsegaye, D., & Colman, J. E. (2019). Mining
activity disturbs habitat use of reindeer in Finnmark, Northern Norway.Polar Biology , 42 (10), 1849–1858.
https://doi.org/10.1007/s00300-019-02563-8
Eldridge, A., Guyot, P., Moscoso, P., Johnston, A., Eyre-Walker, Y., &
Peck, M. (2018). Sounding out ecoacoustic metrics: Avian species
richness is predicted by acoustic indices in temperate but not tropical
habitats. Ecological Indicators , 95 (1), 939–952.
https://doi.org/10.1016/j.ecolind.2018.06.012
Elise, S., Bailly, A., Urbina-Barreto, I., Mou-Tham, G., Chiroleu, F.,
Vigliola, L., Robbins, W. D., & Bruggemann, J. H. (2019). An optimised
passive acoustic sampling scheme to discriminate among coral reefs’
ecological states. Ecological Indicators , 107 (February),
105627. https://doi.org/10.1016/j.ecolind.2019.105627
Erbe, C., Marley, S. A., Schoeman, R. P., Smith, J. N., Trigg, L. E., &
Embling, C. B. (2019). The effects of ship noise on marine mammals—A
review. Frontiers in Marine Science , 6, 6 06.
https://doi.org/10.3389/fmars.2019.00606
Fairbrass, A. J., Rennert, P., Williams, C., Titheridge, H., & Jones,
K. E. (2017). Biases of acoustic indices measuring biodiversity in urban
areas. Ecological Indicators , 83 , 169–177.
https://doi.org/10.1016/j.ecolind.2017.07.064
Farina, A., & Gage, S. H. (2017). Ecoacoustics: the ecological role of
sounds. In A. Farina & S. H. Gage (Eds.), Ecoacoustics: The
ecological role of sounds, 1-366 . John Wiley & Sons, Ltd.
https://doi.org/10.1002/9781119230724
Filatova, O. A., Miller, P. J. O., Yurk, H., Samarra, F. I. P., Hoyt,
E., Ford, J. K. B., Matkin, C. O., & Barrett-Lennard, L. G. (2015).
Killer whale call frequency is similar across the oceans, but varies
across sympatric ecotypes. The Journal of the Acoustical Society
of America , 138 (1), 251–257. https://doi.org/10.1121/1.4922704
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
Geiger, J. T., Schuller, B., & Rigoll, G. (2013). Large-scale audio
feature extraction and SVM for acoustic scene classification.2013 IEEE Workshop on Applications of Signal Processing to
Audio and Acoustics , 1–4. https://doi.org/10.1109/WASPAA.2013.6701857
Gibb, R., Browning, E., Glover‐Kapfer, P., & Jones, K. E. (2019).
Emerging opportunities and challenges for passive acoustics in
ecological assessment and monitoring. Methods in Ecology and
Evolution , 10 (2), 169–185.
https://doi.org/10.1111/2041-210X.13101
Gómez, W. E., Isaza, C. V, & Daza, J. M. (2018). Identifying disturbed
habitats: A new method from acoustic indices. Ecological
Informatics , 45 , 16–25.
https://doi.org/10.1016/j.ecoinf.2018.03.001
Heath, B. E., Sethi, S. S., Orme, C. D. L., Ewers, R. M., & Picinali,
L. (2021). How index selection, compression, and recording schedule
impact the description of ecological soundscapes. Ecology and
Evolution , 11 (19), 13206–13217.
https://doi.org/10.1002/ece3.8042
Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A.,
Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B.,
Slaney, M., Weiss, R. J., & Wilson, K. (2017). CNN architectures for
large-scale audio classification. 2017 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP) ,
131–135. https://doi.org/10.1109/ICASSP.2017.7952132
Houegnigan, L., Safari, P., Nadeu, C., Van Der Schaar, M., & Andre, M.
(2017). A novel approach to real-time range estimation of underwater
acoustic sources using supervised machine learning. OCEANS 2017 -
Aberdeen , 1–5. https://doi.org/10.1109/OCEANSE.2017.8084914
Kowarski, K. A., & Moors-Murphy, H. (2020). A review of big data
analysis methods for baleen whale passive acoustic monitoring.Marine Mammal Science , 37(2) , 652–673.
https://doi.org/10.1111/mms.12758
Kunc, H. P., McLaughlin, K. E., & Schmidt, R. (2016). Aquatic noise
pollution: implications for individuals, populations, and ecosystems.Proceedings of the Royal Society B: Biological Sciences ,283 (1836), 20160839. https://doi.org/10.1098/rspb.2016.0839
Kunc, H. P., & Schmidt, R. (2019). The effects of anthropogenic noise
on animals: A meta-analysis. Biology Letters , 15 (11),
20190649. https://doi.org/10.1098/rsbl.2019.0649
LeBien, J., Zhong, M., Campos-Cerqueira, M., Velev, J. P., Dodhia, R.,
Ferres, J. L., & Aide, T. M. (2020). A pipeline for identification of
bird and frog species in tropical soundscape recordings using a
convolutional neural network. Ecological Informatics , 59 ,
101113. https://doi.org/10.1016/j.ecoinf.2020.101113
Lemaître, G., Nogueira, F., & Aridas, C. K. (2017). Imbalanced-learn: a
python toolbox to tackle the curse of imbalanced datasets in machine
learning. Journal of Machine Learning Research , 18 (1),
559–563. http://jmlr.org/papers/v18/16-365.html
Luís, A. R., Collado, L. J. M., Gospić, N. R., Gridley, T., Papale, E.,
& Azevedo, A. (2021). Vocal universals and geographic variations in the
acoustic repertoire of the common bottlenose dolphin. Scientific
Reports , 11 (1), 1–9. https://doi.org/10.1038/s41598-021-90710-9
Lumini, A., Nanni, L., & Maguolo, G. (2019). Deep learning for plankton
and coral classification. Applied Computing and Informatics .
https://doi.org/10.1016/j.aci.2019.11.004
Luo, J., Siemers, B. M., & Koselj, K. (2015). How anthropogenic noise
affects foraging. Global Change Biology , 21 (9),
3278–3289. https://doi.org/10.1111/gcb.12997
Madhusudhana, S. K., Chakraborty, B., & Latha, G. (2019). Humpback
whale singing activity off the Goan coast in the Eastern Arabian Sea.Bioacoustics , 28 (4), 329–344.
https://doi.org/10.1080/09524622.2018.1458248
McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform
Manifold Approximation and Projection for Dimension Reduction . Retrived
from: http://arxiv.org/abs/1802.03426
Merchant, N. D., Fristrup, K. M., Johnson, M. P., Tyack, P. L., Witt, M.
J., Blondel, P., & Parks, S. E. (2015). Measuring acoustic habitats.Methods in Ecology and Evolution , 6 (3), 257–265.
https://doi.org/10.1111/2041-210X.12330
Merchant, N. D., Pirotta, E., Barton, T. R., & Thompson, P. M. (2014).
Monitoring ship noise to assess the impact of coastal developments on
marine mammals. Marine Pollution Bulletin , 78 (1–2),
85–95. https://doi.org/10.1016/j.marpolbul.2013.10.058
Minello, M., Calado, L., & Xavier, F. C. (2021). Ecoacoustic indices in
marine ecosystems: a review on recent developments, challenges, and
future directions. ICES Journal of Marine Science , 78 (9),
3066–3074. https://doi.org/10.1093/icesjms/fsab193
Mishachandar, B., & Vairamuthu, S. (2021). Diverse ocean noise
classification using deep learning. Applied Acoustics ,181 , 108141. https://doi.org/10.1016/j.apacoust.2021.108141
Nguyen Hong Duc, P., Cazau, D., White, P. R., Gérard, O., Detcheverry,
J., Urtizberea, F., & Adam, O. (2021). Use of ecoacoustics to
characterize the marine acoustic environment off the North Atlantic
French Saint-Pierre-et-Miquelon Archipelago. Journal of Marine
Science and Engineering , 9 (2), 177.
https://doi.org/10.3390/jmse9020177
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S.,
Packer, C., & Clune, J. (2018). Automatically identifying, counting,
and describing wild animals in camera-trap images with deep learning.Proceedings of the National Academy of Sciences of the United
States of America , 115 (25), E5716–E5725.
https://doi.org/10.1073/pnas.1719367115
Padovese, B., Frazao, F., Kirsebom, O. S., & Matwin, S. (2021). Data
augmentation for the classification of North Atlantic right whales
upcalls. The Journal of the Acoustical Society of America ,149 (4), 2520–2530. https://doi.org/10.1121/10.0004258
Plourde, S., Lehoux, C., Johnson, C. L., Perrin, G., & Lesage, V.
(2019). North Atlantic right whale (Eubalaena glacialis ) and its
food: (I) a spatial climatology of Calanus biomass and potential
foraging habitats in Canadian waters. Journal of Plankton
Research , 41 (5), 667–685. https://doi.org/10.1093/plankt/fbz024
Root-Gutteridge, H., Cusano, D. A., Shiu, Y., Nowacek, D. P., Van
Parijs, S. M., & Parks, S. E. (2018). A lifetime of changing calls:
North Atlantic right whales, Eubalaena glacialis , refine
call production as they age. Animal Behaviour , 137 ,
21–34. https://doi.org/10.1016/j.anbehav.2017.12.016
Rycyk, A. M., Tyson Moore, R. B., Wells, R. S., McHugh, K. A., Berens
McCabe, E. J., & Mann, D. A. (2020). Passive Acoustic Listening
Stations (PALS) show rapid onset of ecological effects of harmful algal
blooms in real time. Scientific Reports , 10 (1), 17863.
https://doi.org/10.1038/s41598-020-74647-z
Schmidt, R., Morrison, A., & Kunc, H. P. (2014). Sexy voices - no
choices: Male song in noise fails to attract females. Animal
Behaviour , 94 , 55–59.
https://doi.org/10.1016/j.anbehav.2014.05.018
Sethi, S. S., Jones, N. S., Fulcher, B. D., Picinali, L., Clink, D. J.,
Klinck, H., Orme, C. D. L., Wrege, P. H., & Ewers, R. M. (2020).
Characterizing soundscapes across diverse ecosystems using a universal
acoustic feature set. Proceedings of the National Academy of
Sciences , 117 (29), 17049–17055.
https://doi.org/10.1073/pnas.2004702117
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks
for large-scale image recognition. Retrived from:https://doi.org/10.48550/arXiv.1409.1556
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G., & van Woesik,
R. (2019). A global analysis of coral bleaching over the past two
decades. Nature Communications , 10 (1), 1264.
https://doi.org/10.1038/s41467-019-09238-2
Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J.,
Vercauteren, K. C., Snow, N. P., Halseth, J. M., Di Salvo, P. A., Lewis,
J. S., White, M. D., Teton, B. (2019). Machine learning to classify
animal species in camera trap images: Applications in ecology.Methods in Ecology and Evolution , 10 (4), 585–590.
https://doi.org/10.1111/2041-210X.13120
Usman, A. M., Ogundile, O. O., & Versfeld, D. J. J. (2020). Review of
automatic detection and classification techniques for cetacean
vocalization. IEEE Access , 8 , 105181–105206.
https://doi.org/10.1109/ACCESS.2020.3000477
Vester, H., Hallerberg, S., Timme, M., & Hammerschmidt, K. (2017).
Vocal repertoire of long-finned pilot whales (Globicephalamelas ) in northern Norway. The Journal of the Acoustical
Society of America , 141 (6), 4289–4299.
https://doi.org/10.1121/1.4983685