Abstract
Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70–30% training testing and 89.6% on 5-fold cross-validation experiments.Keywords:
marine mammals, 1D Local Binary Patterns, Mel frequency cepstral coefficients, feature extraction, passive acoustic monitoringReferences
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12. Bhalke D.G., Rajesh B., Bormane D.S. (2017), Automatic genre classification using fractional fourier transform based mel frequency cepstral coefficient and timbral features, Archives of Acoustics, 42(2): 213–222, https://doi.org/10.1515/aoa-2017-0024
13. Bhalke D.G., Rao C.R., Bormane D. (2016), Hybridisation of mel frequency cepstral coefficient and higher order spectral features for musical instruments classification. Archives of Acoustics, 41(3): 427–436, https://doi.org/10.1515/aoa-2016-0042
14. Binder C.M., Hines P.C. (2014), Automated aural classification used for inter-species discrimination of cetaceans, The Journal of the Acoustical Society of America, 135(4): 2113–2125, https://doi.org/10.1121/1.4868378
15. Bort J., Van Parijs S.M., Stevick P.T., Summers E., Todd S. (2015), North Atlantic right whale Eubalaena glacialis vocalization patterns in the central Gulf of Maine from October 2009 through October 2010, Endangered Species Research, 26(3): 271–280, https://doi.org/10.3354/esr00650
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19. Chatlani N., Soraghan J.J. (2010), Local binary patterns for 1-D signal processing, [In:] 18th European Signal Processing Conference, pp. 95–99.
20. Chaudhari R. H., Waghmare K., Gawali B.W. (2015), Accent recognition using MFCC and LPC with acoustic features, International Journal of Innovative Research in Computer and Communication Engineering, 3(3): 2128–2134, https://doi.org/10.15680/ijircce.2015.0303078
21. Das B.P., Parekh R. (2012), Recognition of isolated words using features based on LPC, MFCC, ZCR and STE, with neural network classifiers, International Journal of Modern Engineering Research, 2(3): 854–858.
22. Dash K., Padhi D., Panda B., Mohanty S. (2012), Speaker identification using Mel frequency cepstral coefficient and BPNN, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, 2(4): 326–332, .
23. Erbe C. et al. (2017), Review of underwater and in-air sounds emitted by Australian and Antarctic marine mammals, Acoustics Australia, 45(2): 179–241, https://doi.org/10.1007/s40857-017-0101-z
24. Feroze K., Sultan S., Shahid S., Mahmood F. (2018), Classification of underwater acoustic signals using multi-classifiers, [In:] 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 723–728, https://doi.org/10.1109/IBCAST.2018.8312302
25. Frankel A.S., Yin S. (2010), A description of sounds recorded from melon-headed whales (Peponocephala electra), off Hawai‘i, The Journal of the Acoustical Society of America, 127(5): 3248–3255, https://doi.org/10.1121/1.3365259
26. Frasier K.E., Roch M.A., Soldevilla M.S., Wiggins S.M., Garrison L.P., Hildebrand J.A. (2017), Automated classification of dolphin echolocation click types from the Gulf of Mexico, PLoS Computational Biology, 13(12): e1005823, https://doi.org/10.1371/journal.pcbi.1005823
27. González-Hernández F.R., Sánchez-Fernández L.P., Suárez-Guerra S., Sánchez-Pérez L.A. (2017), Marine mammal sound classification based on a parallel recognition model and octave analysis, Applied Acoustics, 119: 17–28, https://doi.org/10.1016/j.apacoust.2016.11.016
28. Guisan A., Edwards T.C., Hastie T. (2002), Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2–3): 89–100, https://doi.org/10.1016/S0304-3800%2802%2900204-1
29. Hsu S.-K. et al. (2007), Marine Cable Hosted Observatory (MACHO) project in Taiwan, [In:] 2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies, pp. 305–307, https://doi.org/10.1109/UT.2007.370808
30. Ibrahim A.K. et al. (2018), An approach for automatic classification of grouper vocalizations with passive acoustic monitoring, The Journal of the Acoustical Society of America, 143(2): 666–676, https://doi.org/10.1121/1.5022281
31. Irtaza A., Adnan S.M., Aziz S., Javed A., Ullah M.O., Mahmood M.T. (2017), A framework for fall detection of elderly people by analyzing environmental sounds through acoustic local ternary patterns, [In:] 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1558–1563, https://doi.org/10.1109/SMC.2017.8122836
32. Janik, V. M. (2013), Cognitive skills in bottlenose dolphin communication, Trends in Cognitive Sciences, 17(4): 157–159, https://doi.org/10.1016/j.tics.2013.02.005
33. Kaniklides S. (2014), Effects of Volcanic Tsunamis on Marine Mammals, Thesis for: PhD, https://doi.org/10.13140/RG.2.1.4696.1687
34. Khan M.U., Aziz S., Amjad F., Mohsin M. (2019a), Detection of dilated cardiomyopathy using pulse plethysmographic signal analysis, [In:] 2019 22nd International Multitopic Conference (INMIC), pp. 1–5, https://doi.org/10.1109/INMIC48123.2019.9022734
35. Khan M. U., Aziz S., Ibraheem S., Butt A., Shahid H. (2019b), Characterization of term and preterm deliveries using electrohysterograms signatures, [In:] 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0899–0905, https://doi.org/10.1109/IEMCON.2019.8936292
36. Lin T.-H., Chou L.-S. (2015), Automatic classification of delphinids based on the representative frequencies of whistles, The Journal of the Acoustical Society of America, 138(2): 1003–1011, https://doi.org/10.1121/1.4927695
37. Lin T.-H., Chou L.-S., Akamatsu T., Chan H.-C., Chen C.-F. (2013), An automatic detection algorithm for extracting the representative frequency of cetacean tonal sounds, The Journal of the Acoustical Society of America, 134(3): 2477–2485, https://doi.org/10.1121/1.4816572
38. Lin T.-H., Yu H.-Y., Chou L.-S., Chen C.-F. (2014), Passive acoustic monitoring on the seasonal species composition of cetaceans from the marine cable hosted observatory, [In:] OCEANS 2014-TAIPEI, pp. 1–6, https://doi.org/10.1109/OCEANS-TAIPEI.2014.6964392
39. López B. D., Addis A., Fabiano F. (2013), Ecology of common bottlenose dolphins along the North-western Sardinian coastal waters (Italy), Thalassas: An International Journal of Marine Sciences, 29(2): 35–44.
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