Acoustic Identification of Dolphin Whistle Types in Deep Waters of Arabian Sea Using Wavelet Threshold Denoising Approach

Mahanty, Madan Mohan; Cheenankandy, Sanjana M.; Latha, Ganesan; Raghuraman, Govindan; Venkatesan, Ramasamy

doi:10.24425/aoa.2023.144264

Artykuł - szczegóły

Tytuł artykułu

Acoustic Identification of Dolphin Whistle Types in Deep Waters of Arabian Sea Using Wavelet Threshold Denoising Approach

Autorzy

Mahanty Madan Mohan , Cheenankandy Sanjana M. , Latha Ganesan , Raghuraman Govindan , Venkatesan Ramasamy

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.24425/aoa.2023.144264

Warianty tytułu

Języki publikacji

Abstrakty

In situ time series measurements of ocean ambient noise, have been made in deep waters of the Arabian Sea, using an autonomous passive acoustic monitoring system deployed as part of the Ocean Moored buoy network in the Northern Indian Ocean (OMNI) buoy mooring operated by the National Institute of Ocean Technology (NIOT), in Chennai during November 2018 to November 2019. The analysis of ambient noise records during the spring (April–June) showed the presence of dolphin whistles but contaminated by unwanted impulsive shackle noise. The frequency contours of the dolphin whistles occur in narrow band in the range 4–16 kHz. However, the unwanted impulsive shackle noise occurs in broad band with the noise level higher by ~20 dB over the dolphin signals, and it reduces the quality of dolphin whistles. A wavelet based threshold denoising technique followed by a subtraction method is implemented. Reduction of unwanted shackle noise is effectively done and different dolphin whistle types are identified. This wavelet denoising approach is demonstrated for extraction of dolphin whistles in the presence of challenging impulsive shackle noise. Furthermore, this study should be useful for identifying other cetacean species when the signal of interest is interrupted by unwanted mechanical noise.

Słowa kluczowe

deep water ambient noise Arabian Sea wavelet threshold denoising impulsive shackle noise dolphin whistle types

Wydawca

Instytut Podstawowych Problemów Techniki PAN
Komitet Akustyki PAN
Polskie Towarzystwo Akustyczne

Czasopismo

Archives of Acoustics

Rocznik

2023

Tom

Vol. 48, No. 1

Strony

39--48

Opis fizyczny

Bibliogr. 57 poz., rys., tab., wykr.

Twórcy

autor

Mahanty Madan Mohan

mmmahanty@gmail.com

National Institute of Ocean Technology, Ministry of Earth Sciences Chennai, India

autor

Cheenankandy Sanjana M.

National Institute of Ocean Technology, Ministry of Earth Sciences Chennai, India

autor

Latha Ganesan

National Institute of Ocean Technology, Ministry of Earth Sciences Chennai, India

autor

Raghuraman Govindan

National Institute of Ocean Technology, Ministry of Earth Sciences Chennai, India

autor

Venkatesan Ramasamy

National Institute of Ocean Technology, Ministry of Earth Sciences Chennai, India

Bibliografia

1. Acevedo-Gutiérrez A., Stienessen S.C. (2004), Bottlenose dolphins (Tursiops truncatus) increase number of whistles when feeding, Aquatic Mammals, 30: 357-362, doi: 10.1578/AM.30.3.2004.357.
2. Akiyama J., Ohta M. (2007), Increased number of whistles of bottlenose dolphins, Tursiops truncatus, arising from interaction with people, Journal of Veterinary Medical Science, 69(2): 165-170, doi: 10.1292/jvms.69.165.
3. Au W.W.L. (1993), Characteristics of dolphin sonar signals, [in:] The Sonar of Dolphins, pp. 115-139, Springer, New York, doi: 10.1007/978-1-4612-4356-4_7.
4. Azevedo F.A., Oliveira A.M., Dalla Rosa L., Lailson-Brito J. (2007), Characteristics of whistles from resident bottlenose dolphins (Tursiops truncatus) in southern Brazil, The Journal of Acoustical Society of America, 121(5): 2978-2983, doi: 10.1121/1.2713726.
5. Beslin W.A., Whitehead H., Gero S. (2018), Automatic acoustic estimation of sperm whale size distributions achieved through machine recognition of on-axis clicks, The Journal of Acoustical Society of America, 144(6): 3485-3495, doi: 10.1121/1.5082291.
6. Bey N.Y. (2006), Extraction of signals buried in noise. Part I: Fundamentals, Signal Processing, 86(9): 2464-2478, doi: 10.1016/j.sigpro.2005.11.014.
7. Boisseau O. (2005), Quantifying the acoustic repertoire of a population: The vocalizations of free-ranging bottlenose dolphins in Fiordland, New Zealand, The Journal of Acoustical Society of America, 117(4): 2318-2329, doi: 10.1121/1.1861692.
8. Caldwell M.C., Caldwell D.K., Tyack P.L. (1990), Review of the signature whistle hypothesis for the Atlantic bottlenose dolphin, [in:] Leatherwood S., Reeves R.R. [Eds.], The Bottlenose Dolphin, pp. 199-234, Amsterdam, Elsevier, doi: 10.1016/B978-0-12-440280-5.50014-7.
9. Chang S.G., Yu B., Vetterli M. (2000), Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, 9(9): 1532-1546, doi: 10.1109/83.862633.
10. Chen J., Benesty J., Huang Y., Doclo S. (2006), New insights into the noise reduction Wiener filter, IEEE Transactions on Audio, Speech, and Language Processing, 14(4): 1218-1234, doi: 10.1109/TSA.2005.860851.
11. Clark R.A. et al. (2012), Cetacean sightings and acoustic detections in the offshore waters of the Maldives during the northeast monsoon seasons of 2003 and 2004, Journal of Cetacean Research and Management, 12(2): 227-234.
12. Corkeron P.J., Van Parijs S.M. (2001), Marine mammal migrations and movement patterns, [in:] Steele J.H., Turekian K.K., Thorpe S.A. [Eds.], Encyclopedia of Ocean Sciences, Academic Press, pp. 596-604, Oxford, doi: 10.1016/B978-012374473-9.00442-2.
13. Daubechies I. (1992), Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, USA, doi: 10.1137/1.9781611970104.
14. Donoho L.D., Johnstone I.M. (1995), Adatpting to unknow smoothness via wavelet shrinkage, Journal of the American Statistical Association, 90(432): 1200-1224, doi: 10.1515/9781400827268.833.
15. Donoho L.D., Johnstone M.J. (1994), Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81: 425-455, doi: 10.1093/biomet/81.3.425.
16. Dragomiretskiy K., Zosso D. (2013), Variational mode decomposition, IEEE Transactions on Signal Processing, 62(3): 531-544, doi: 10.1109/TSP.2013.2288675.
17. Esch H.C., Sayigh L.S., Blum J.E., Wells R.S. (2009), Whistles as potential indicators of stress in bottlenose dolphins (Tursiops truncatus), Journal of Mammalogy, 90(3): 638-650, doi: 10.1644/08-MAMMA-069R.1.
18. Esch H.C., Sayigh L.S., Wells R.S. (2009), Quantifying parameters of bottlenose dolphin signature whistles, Marine Mammal Science, 45(4): 976-986, doi: 10.1111/j.1748-7692.2009.00289.x.
19. Heiler J., Elwen S.H., Kriesell H.J., Gridley T. (2016), Changes in bottlenose dolphin whistle parameters related to vessel presence, surface behaviour and group composition, Animal Behavior, 117: 167-177, doi: 10.1016/j.anbehav.2016.04.014.
20. Hunynh Q.Q., Cooper L.N., Intrator N., Shouval H. (1998), Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory, IEEE Transactions on Signal Processing, 46(5): 1202-1207, doi: 10.1109/78.668783.
21. Isabona J., Azi S. (2012), OptimisedWalficsh-Bertoni Model for pathloss prediction in urban propagation environment, International Journal of Engineering and Innovative Technology, 2(5): 14-20.
22. Janik V.M. (2009), Acoustic communication in delphinids [in:] Naguib M., Janik V.M. [Eds.], Advances in the Study of Behavior, 40: 123-157, doi: 10.1016/S0065-3454(09)40004-4.
23. Janik V.M., King S.L., Sayigh L.S., Wells R.S. (2013), Identifying signature whistles from recordings of groups of unrestrained bottlenose dolphins (Tursiops truncatus), Marine Mammal Science, 29(1): 109-122, doi: 10.1111/j.1748-7692.2011.00549.x.
24. Janik V.M., Sayigh L.S., Wells R.S. (2006), Signature whistle shape conveys identity information to bottlenose dolphins, Proceedings of the National Academy of Sciences of the United States of America, 103(21): 8293-8297, doi: 10.1073/pnas.0509918103.
25. Janik V.M., Slater P. (1998), Context-specific use suggests that bottlenose dolphin signature whistles are cohesion calls, Animal Behaviour, 56(4): 829-838, doi: 10.1006/anbe.1998.0881.
26. Janik V.M., Todt D., Dehnhardt G. (1994), Signature whistle variations in a bottlenosed dolphin, Tursiops truncatus, Behavioral Ecology and Sociobiology, 35(4): 243-248, doi: 10.1007/BF00170704.
27. Jones B., Zapetis M., Samuelson M.M., Ridgway S. (2020), Sounds produced by bottlenose dolphins (Tursiops): a review of the defining characteristics and acoustic criteria of the dolphin vocal repertoire, Bioacoustics, 29(4): 399-440, doi: 10.1080/09524622.2019.1613265.
28. Khan M.M., Ashique R.H., Liya B.N., Sajjad M.M., Rahman M.A., Amin M.H. (2015), New wavelet thresholding algorithm in dropping ambient noise from underwater acoustic signals, Journal of Electromagnetic Analysis and Applications, 7(3): 53-60, doi: 10.4236/jemaa.2015.73006.
29. King S.L., Allen S.J., Krützen M., Connor R.C. (2019), Vocal behaviour of allied male dolphins during cooperative mate guarding, Animal Cognition, 22(6): 991-1000, doi: 10.1007/s10071-019-01290-1.
30. Kriesell H.J., Elwen S.H., Nastasi A., Gridley T. (2014), Identification and characteristics of signature whistles in wild bottlenose dolphins (Tursiops truncatus) from Namibia, PLOS ONE, 9(9): e106317, doi: 10.1371/journal.pone.0106317.
31. Learned R.E., Willsky A.S. (1995), A wavelet packet approach to transient signal classification, Applied and Computational Harmonic Analysis, 2(3): 265-278, doi: 10.1006/acha.1995.1019.
32. Li N., Zhou M. (2008), Audio denoising algorithm based on adaptive wavelet soft-threshold of gain factor and teager energy operator, [in:] 2008 International Conference on Computer Science and Software Engineering, pp. 787-790, doi: 10.1109/CSSE.2008.1523.
33. Lopez-Otero P., Docio-Fernandez L., Cardenal-López A. (2018), Using discrete wavelet transform to model whistle contours for dolphin species classification, Proceedings, 2(18): 1183, doi: 10.3390/proceedings2181183.
34. Lukac R., Smolka B., Plataniotis K.N. (2007), Sharpening vector median filters, Signal Processing, 87(9): 2085-2099, doi: 10.1016/j.sigpro.2007.02.009.
35. Mallat S.G. (1989), A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7): 674-693, doi: 10.1109/34.192463.
36. Mallawaarachchi A., Ong S.H., Chitre M., Taylor E. (2008), Spectrogram denoising and automated extraction of the fundamental frequency variation of dolphin whistles, The Journal of Acoustical Society of America, 124(2): 1159-1170, doi: 10.1121/1.2945711.
37. Mallik T.K. (2017), Coral atolls of Lakshadweep, Arabian Sea, Indian Ocean, MOJ Ecology Environmental Sciences, 2(2): 68-83, doi: 10.15406/mojes.2017.02.00021.
38. Marley S.A., Salgado-Kent C.P., Erbe C., Parnum I. (2017), Effects of vessel traffic and underwater noise on the movement, behaviour and vocalisations of bottlenose dolphins in an urbanised estuary, Scientific Reports, 7: 13437, doi: 10.1038/s41598-017-13252-z.
39. Math Works (n.d.), Wavelet Interval-Dependent Denoising, https://in.mathworks.com/help/wavelet/ug/wavelet-interval-dependent-denoising.html.
40. Panicker D., Sutaria D., Kumar A., Stafford K.M. (2020), Cetacean distribution and diversity in Lakshadweep waters, India, using a platform of opportunity: October 2015 to April 2016, Aquatic Mammals, 46(1): 80-92, doi: 10.1578/AM.46.1.2020.80.
41. Pillai C.S.G., Jasmine S. (1989), The coral fauna of Lakshadweep, [in:] James P.S.B.R., Suseelan C. [Eds.], Marine Living Resources of the Union Territory of Lakshadweep: An Indicative Survey with Suggestions or Development, Central Marine Fisheries Research Institute, 43: 179-195.
42. Powell K.J., Sapatinas T., Bailey T.C., Krzanowski W.J. (1995), Application of wavelets to the pre-processing of underwater sounds, Statistics and Computing, 5: 265-273.
43. Prakash T.N., Nair L.S., Hameed T.S.S. (2015), Geomorphology and Physical Oceanography of the Lakshadweep Coral Islands in the Indian Ocean, Springer Cham, doi: 10.1007/978-3-319-12367-7.
44. Quick N.J., Janik V.M. (2012), Bottlenose dolphins exchange signature whistles when meeting at sea, Proceedings of the Royal Society B: Biological Sciences, 279: 2539-2545, doi: 10.1098/rspb.2011.2537.
45. Rachinas-Lopes P., Luís A.R., Borges A.S., Neto M., dos Santos M.E. (2017), Whistle stability and variation in captive bottlenose dolphins (Tursiops truncatus) recorded in isolation and social contexts, Aquatic Mammal, 43(1): 1-13, doi: 10.1578/AM.43.1.2017.1.
46. Rowe A.C., Abbott P.C. (1995), Daubechies wavelets and mathematica, Computers in Physics, 9(6): 635-648, doi: 10.1063/1.168556.
47. Sayigh L.S., Tyack P.L., Wells R.S., Solow A.R., Scott M.D., Irvine A.B. (1999), Individual recognition in wild bottlenose dolphins: A field test using playback experiments, Animal Behaviour, 57(1): 41-50, doi: 10.1006/anbe.1998.0961.
48. Seramani S., Taylor E.A., Seekings P.J., Yeo K.P. (2006), Wavelet de-noising with independent component analysis for segmentation of dolphin whistles in a noisy underwater environment, [in:] OCEANS 2006 – Asia Pacific, pp. 1-7, doi: 10.1109/OCEANSAP.2006.4393920.
49. Slater P.J.B. (1983), The study of communication, [in:] Halliday T.R., Slater P.J.B [Eds.], Animal Behaviour, Communication, 2nd ed., pp. 9-42, Blackwell, Oxford.
50. Smolker R.A., Mann J., Smuts B.B. (1993), Use of signature whistles during separations and reunions by wild bottlenose dolphin mothers and infants, Behavioral Ecology and Sociobiology, 33(6): 393-402, doi: 10.1007/BF00170254.
51. Tikkanen P.E. (1999), Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal, Biological cybernetics, 80(4): 259-267, doi: 10.1007/s004220050523.
52. Ukte A., Kizilkaya A., Elbi M.D. (2014), Two empirical methods for improving the performance of statistical multirate high-resolution signal reconstruction, Digital Signal Processing, 26: 36-49, doi: 10.1016/j.dsp.2013.11.014.
53. de Vos A. et al. (2012), Cetacean sightings and acoustic detections in the offshore waters of Sri Lanka: March–June 2003, Journal of Cetacean Research and Management, 12(2): 185-193.
54. Wang D., Würsig B., Evans W.E. (1995), Whistles of bottlenose dolphins: Comparisons among populations, Aquatic Mammals, 21: 65-77.
55. Xiang L.W., Wang W.B. (2015), Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform, Chinese Physics B, 24(8): 080203, doi: 10.1088/16741056/24/8/080203.
56. Yu G., Bacry E., Mallat S. (2007), Audio signal denoising with complex wavelets and adaptive block attenuation, [in:] IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’07, 3: 869-872, doi: 10.1109/ICASSP.2007.366818.
57. Zhang X., Xiong Y. (2009), Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter, IEEE Signal Processing Letters, 16(4): 295-298, doi: 10.1109/LSP.2009.2014293

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-48129a30-452b-4e87-8baa-9dfada4ecb5d