Marine Mammals Classification using Acoustic Binary Patterns

• Autorzy: Nadir Maheen , Adnan Syed M. , Aziz Sumair , Khan Muhammad Umar

Identyfikatory

DOI

10.24425/aoa.2020.135278

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

marine mammals; 1D Local Binary Patterns; Mel frequency cepstral coefficients; feature extraction; passive acoustic monitoring

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 4
• Strony: 721--731
• Opis fizyczny: Bibliogr. 63 poz., rys., tab., wykr.

Twórcy

autor

Nadir Maheen

• Department of Computer Sciences, University of Engineering and Technology, Taxila, Pakistan

autor

Adnan Syed M.

• Department of Computer Sciences, University of Engineering and Technology, Taxila, Pakistan

autor

Aziz Sumair

• Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan

autor

Khan Muhammad Umar

• Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan

Bibliografia

• 1. Aida-Zade K., Ardil C., Rustamov S. (2006), Investigation of combined use of MFCC and LPC features in speech recognition systems, World Academy of Science, Engineering and Technology, 19: 74-80.
• 2. Amin A., Thomas D. (1996), The negotiated economy: state and civic institutions in Denmark, Economy and Society, 25: 255-281.
• 3. Aziz S., Awais M., Akram T., Khan U., Alhussein M., Aurangzeb K. (2019a), Automatic scene recognition through acoustic classification for behavioral robotics, Electronics, 8 (5): 483; doi: 10.3390/electronics8050483.
• 4. Aziz S., Khan M. U., Aamir F., Javid M. A. (2019b), Electromyography (EMG), data-driven load classification using empirical mode decomposition and feature analysis, [in:] 2019 International Conference on Frontiers of Information Technology (FIT), pp. 272-2725, doi: 10.1109/FIT47737.2019.00058.
• 5. Aziz S., Khan M. U., Choudhry Z. A., Aymin A., Usman A. (2019c), ECG-based Biometric authentication using empirical mode decomposition and support vector machines, [in:] 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0906-0912, doi: 10.1109/IEMCON.2019.8936174.
• 6. Aziz S., Khan M. U., Shakeel M., Mushtaq Z., Khan A. Z. (2019d), An automated system towards diagnosis of pneumonia using pulmonary auscultations, [in:] 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), pp. 1-7, doi: 10.1109/MACS48846.2019.9024789.
• 7. Bahoura M., Simard Y. (2010), Blue whale calls classification using short-time Fourier and wavelet packet transforms and artificial neural network, Digital Signal Processing, 20 (4): 1256-1263, doi: 10.1016/j.dsp.2009.10.024.
• 8. Baumann-Pickering S., Wiggins S. M., Hildebrand J. A., Roch M. A., Schnitzler H.-U. (2010), Discriminating features of echolocation clicks of melonheaded whales (Peponocephala electra), bottlenose dolphins (Tursiops truncatus), and Gray’s spinner dolphins (Stenella longirostris longirostris), The Journal of the Acoustical Society of America, 128 (4): 2212-2224, doi: 10.1121/1.3479549.
• 9. Baumgartner M. F., Stafford K. M., Latha G. (2018), Near real-time underwater passive acoustic monitoring of natural and anthropogenic sounds, [in:] Venkatesan R., Tandon A., D’Asaro E., Atmanand M. [Eds], Observing the Oceans in Real Time. Springer Oceanography, pp. 203-226, Springer, Cham, doi: 10.1007/978-3-319-66493-4_10.
• 10. Bearzi G., Reeves R. R., Remonato E., Pierantonio N., Airoldi S. (2011), Risso’s dolphin Grampus griseus in the Mediterranean Sea, Mammalian Biology – Zeitschrift für Säugetierkunde, 76 (4): 385-400, doi: 10.1016/j.mambio.2010.06.003.
• 11. Benoit-Bird K. J., Au W. W. (2009), Phonation behavior of cooperatively foraging spinner dolphins, The Journal of the Acoustical Society of America, 125 (1): 539-546, doi: 10.1121/1.2967477.
• 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, doi: 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, doi: 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, doi: 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, doi: 10.3354/esr00650.
• 16. Boser B. E., Guyon I. M., Vapnik V. N. (1992), A training algorithm for optimal margin classifiers, [in:] Proceedings of the Fifth Annual Workshop On Computational Learning Theory, pp. 144-152, ACM, doi: 10.1145/130385.130401.
• 17. Bougher B. B., Hood J., Theriault J., Moors H. (2012), Generalized marine mammal detection based on improved band-limited processing, Proceedings of Meetings on Acoustics ECUA2012, 17 (1): 070067, doi: 10.1121/1.4773596.
• 18. Bowen W. (1997), Role of marine mammals in aquatic ecosystems, Marine Ecology Progress Series, 158: 267-274.
• 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, doi: 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 inair sounds emitted by Australian and Antarctic marine mammals, Acoustics Australia, 45 (2): 179-241, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.1016/S0304-3800(02)00204-1.
• 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, doi: 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, doi: 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, doi: 10.1109/SMC.2017.8122836.
• 32. Janik V. M. (2013), Cognitive skills in bottlenose dolphin communication, Trends in Cognitive Sciences, 17 (4): 157-159, doi: 10.1016/j.tics.2013.02.005.
• 33. Kaniklides S. (2014), Effects of Volcanic Tsunamis on Marine Mammals, Thesis for: PhD, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.1109/OCEANS-TAIPEI.2014.6964392.
• 39. López B. D., Addis A., Fabiano F. (2013), Ecology of common bottlenose dolphins along the Northwestern Sardinian coastal waters (Italy), Thalassas: An International Journal of Marine Sciences, 29 (2): 35-44.
• 40. Luo W., Yang W., Song Z., Zhang Y. (2017), Automatic species recognition using echolocation clicks from odontocetes, [in:] 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1-5, doi: 10.1109/ICSPCC.2017.8242503.
• 41. McCool P., Chatlani N., Petropoulakis L., Soraghan J. J., Menon R., Lakany H. (2012), 1-D local binary patterns for onset detection of myoelectric signals, [In:] 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 499-503.
• 42. Mellinger D. K., Clark C. W. (2000), Recognizing transient low-frequency whale sounds by spectrogram correlation, The Journal of the Acoustical Society of America, 107 (6): 3518-3529, doi: 10.1121/1.429434.
• 43. Mellinger D. K., Clark C. W. (2006), MobySound: A reference archive for studying automatic recognition of marine mammal sounds, Applied Acoustics, 67 (11-12): 1226-1242, doi: 10.1016/j.apacoust.2006.06.002.
• 44. Mellinger D. K. et al. (2017), Advanced methods for passive acoustic detection, classification, and localization of marine mammals, The Journal of the Acoustical Society of America, 141 (5): 3604-3604, doi: 10.1121/1.4987711.
• 45. Morales S., Engan K., Naranjo V., Colomer A. (2017), Retinal disease screening through local binary patterns, IEEE Journal of Biomedical and Health Informatics, 21 (1): 184-192, doi: 10.1109/JBHI.2015.2490798.
• 46. Nalavade K., Meshram B. (2012), Data classification using support vector machine, [in:] National Conference on Emerging Trends in Engineering & Technology (VNCET), pp. 181-184.
• 47. Ojala T., Pietikäinen M., Harwood D. (1996), A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, 29 (1): 51-59, doi: 10.1016/0031-3203(95)00067-4.
• 48. Pal M., Mather P. (2005), Support vector machines for classification in remote sensing, International Journal of Remote Sensing, 26 (5): 1007-1011, doi: 10.1080/01431160512331314083.
• 49. Payne R. S., McVay S. (1971), Songs of humpback whales, Science, 173 (3997): 585-597, doi: 10.1126/science.173.3997.585.
• 50. Qian K. et al. (2018), Teaching machines on snoring: A benchmark on computer audition for snore sound excitation localisation, Archives of Acoustics, 43 (3): 465-475, doi: 10.24425/123918.
• 51. Ramayah T., Ahmad N. H., Halim H. A., May-Chiun S. R. M. Z. (2010), Discriminant analysis: an illustrated example, African Journal of Business Management, 4 (9): 1654-1667, doi: 10.5897/AJBM.9000211.
• 52. Rankin S. et al. (2017), Acoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses, Marine Mammal Science, 33 (2): 520-540, doi: 10.1111/mms.12381.
• 53. Reljin N., Pokrajac D. (2017), Music performers classification by using multifractal features: a case study, Archives of Acoustics, 42 (2): 223-233, doi: 10.1515/aoa-2017-0025.
• 54. Sakthivel M., Gopakumar G., Ramkumar S., Mhatre V., Jose S. T. (2014), Development of protocols for stranding/beaching and post-mortem analysis of cetaceans for the capacity building of officials and local people of Sindhudurg district of Maharashtra, Project Report. Central Marine Fisheries Research Institute, Mandapam, http://eprints.cmfri.org.in/id/eprint/10468.
• 55. Seavy N. E., Quader S., Alexander J. D., Ralph C. J. (2005), Generalized linear models and point count data: statistical considerations for the design and analysis of monitoring studies, [in:] Ralph C. J., Rich T. D. [Eds], Bird Conservation Implementation and Integration in the Americas: Proceedings of the Third International Partners in Flight Conference, March 20-24, 2002; Asilomar, California, Vol. 2, General Technical Report PSW-GTR-191, Albany, CA: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Research Station, p. 744-753.
• 56. Shin K.-S., Lee T. S., Kim H.-J. (2005), An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications, 28 (1): 127-135, doi: 10.1016/j.eswa.2004.08.009.
• 57. Sugumaran V., Muralidharan V., Ramachandran K. (2007), Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing, 21 (2): 930-942, doi: 10.1016/j.ymssp.2006.05.004.
• 58. Tang Z., Su Y., Er M. J., Qi F., Zhang L., Zhou J. (2015), A local binary pattern based texture descriptors for classification of tea leaves, Neurocomputing, 168: 1011-1023, doi: 10.1016/j.neucom.2015.05.024.
• 59. Thode A., Mellinger D. K., Stienessen S., Martinez A., Mullin K. (2002), Depth-dependent acoustic features of diving sperm whales (Physeter macrocephalus) in the Gulf of Mexico, The Journal of the Acoustical Society of America, 112 (1): 308-321, doi: 10.1121/1.1482077.
• 60. Thorne L. H. et al. (2012), Predictive modeling of spinner dolphin (Stenella longirostris), resting habitat in the main Hawaiian Islands, PLoS One, 7 (8): e43167, doi: 10.1371/journal.pone.0043167.
• 61. Tiwari V. (2010), MFCC and its applications in speaker recognition, International Journal on Emerging Technologies, 1 (1): 19-22.
• 62. Valero X., Alías F. (2012), Hierarchical classification of environmental noise sources considering the acoustic signature of vehicle pass-bys, Archives of Acoustics, 37 (4): 423-434.
• 63. Young V. W., Hines P. C. (2007), Perception-based automatic classification of impulsive-source active sonar echoes, The Journal of the Acoustical Society of America, 122 (3): 1502-1517, 10.1121/1.2767001.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).