The work presented in this paper focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. Specifically, it focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. To this end, various indicators can be used to monitor marine areas such as both the geographical and temporal evolution of fish populations. A discriminative model is built using supervised machine learning (random-forest and support-vector machines). Each acquisition is represented in a feature space, in which the patterns belonging to different semantic classes are as separable as possible. The set of features proposed for describing the acquisitions come from an extensive state of the art in various domains in which classification of acoustic signals is performed, including speech, music, and environmental acoustics. Furthermore, this study proposes to extract features from three representations of the data (time, frequency, and cepstral domains). The proposed classification scheme is tested on real fish sounds recorded on several areas, and achieves 96.9% correct classification compared to 72.5% when using reference state of the art features as descriptors. The classification scheme is also validated on continuous underwater recordings, thereby illustrating that it can be used to both detect and classify fish sounds in operational scenarios.
References
1.
Acevedo
, M. A.
, Corrada-Bravo
, C. J.
, Corrada-Bravo
, H.
, Villanueva-Rivera
, L. J.
, and Aide
, T. M.
(2009
). “Automated classification of bird and amphibian calls using machine learning: A comparison of methods
,” Ecol. Inform.
4
(4
), 206
–214
.2.
Amorim
, M. C. P.
(2006
). “Diversity of sound production in fish
,” Commun. Fish.
1
, 71
–104
.3.
Amorim
, M. C. P.
, Stratoudakis
, Y.
, and Hawkins
, A. D.
(2004
). “Sound production during competitive feeding in the grey gurnard
,” J. Fish Biol.
65
(1
), 182
–194
.4.
Bedoya
, C.
, Isaza
, C.
, Daza
, J. M.
, and López
, J. D.
(2014
). “Automatic recognition of anuran species based on syllable identification
,” Ecol. Inform.
24
, 200
–209
.5.
Bellman
, R.
(1956
). “Dynamic programming and Lagrange multipliers
,” Proc. Natl. Acad. Sci. U.S.A.
42
(10
), 767
–769
.6.
Boser
, B. E.
, Guyon
, I. M.
, and Vapnik
, V. N.
(1992
). “A training algorithm for optimal margin classifiers
,” in Proceedings of the Fifth Annual Workshop on Computational Learning
, July 27–29
, Pittsburg, PA
, pp. 144
–152
.7.
Breiman
, L.
(2001
). “Random forests
,” Mach. Learn.
45
, 5
–32
.8.
Chen
, W. P.
, Chen
, S. S.
, Lin
, C. C.
, Chen
, Y. Z.
, and Lin
, W. C.
(2012
). “Automatic recognition of frog calls using a multi-stage average spectrum
,” Comput. Math. Appl.
64
(5
), 1270
–1281
.9.
Chesmore
, E. D.
(2001
). “Application of time domain signal coding and artificial neural networks to passive acoustical identification of animals
,” Appl. Acoust.
62
(12
), 1359
–1374
.10.
Clemins
, P. J.
, and Johnson
, M. T.
(2006
). “Generalized perceptual linear prediction features for animal vocalization analysis
,” J. Acoust. Soc. Am.
120
(1
), 527
–534
.11.
Costanza
, R.
, d'Arge
, R.
, De Groot
, R.
, Farber
, S.
, Grasso
, M.
, Hannon
, B.
, Limburg
, K.
, Naeem
, S.
, O'Neill
, R. V.
, Paruelo
, J.
, Raskin
, R. G.
, Sutton
, P.
, and van den Belt
, M.
(1997
). “The value of the world's ecosystem services and natural capital
,” Nature
387
(6630
), 253
–260
.12.
Couvreur
, C.
, Fontaine
, V.
, Gaunard
, P.
, and Mubikangiey
, C. G.
(1998
). “Automatic classification of environmental noise events by hidden Markov models
,” Appl. Acoust.
54
(3
), 187
–206
.13.
Dennis
, J.
, Tran
, H. D.
, and Li
, H.
(2011
). “Spectrogram image feature for sound event classification in mismatched conditions
,” IEEE Signal Process. Lett.
18
(2
), 130
–133
.14.
Deshpande
, H.
, Singh
, R.
, and Nam
, U.
(2001
). “Classification of music signals in the visual domain
,” in Proceedings of the COST-G6 Conference on Digital Audio Effects
, December 6–8
, Limerick, Ireland
, pp. 1
–4
.15.
Dong
, X.
, Towsey
, M.
, Zhang
, J.
, Banks
, J.
, and Roe
, P.
(2013
). “A novel representation of bioacoustic events for content-based search in field audio data
,” in Proceedings of the 2013 IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA)
, November 26–28
, Hobart, Tasmania
, pp. 1
–6
.16.
Dos Santos
, M. E.
, Modesto
, T.
, Matos
, R. J.
, Grober
, M. S.
, Oliveira
, R. F.
, and Canario
, A.
(2000
). “Sound production by the Lusitanian toad fish, Halobatrachus didactylus
,” Bioacoustics
10
(4
), 309
–321
.17.
Duda
, R. O.
, Hart
, P. E.
, and Stork
, D. G.
(2001
). Pattern Classification
(John Wiley & Sons
, New York
).18.
Eronen
, A.
, and Klapuri
, A.
(2000
). “Musical instrument recognition using cepstral coefficients and temporal features
,” in Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'00
, Vol. 2
, June 5–9
, Istanbul, Turkey
, pp. II753
–II756
.19.
Esfahanian
, M.
, Zhuang
, H.
, and Erdol
, N.
(2014
). “Sparse representation for classification of dolphin whistles by type
,” J. Acoust. Soc. Am.
136
, EL1
–EL7
.20.
Esmaili
, S.
, Krishnan
, S.
, and Raahemifar
, K.
(2004
). “Content based audio classification and retrieval using joint time-frequency analysis
,” in Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
, Vol. 5
, May 17–21
, Montreal, Canada
, pp. 9
–12
.21.
Fagerlund
, S.
(2007
). “Bird species recognition using support vector machines
,” EURASIP J. Adv. Signal Process.
2007
, 038637
.22.
Foote
, J.
(1997
). “A similarity measure for automatic audio classification
,” in Proceedings of the AAAI 1997 Spring Symposium on Intelligent Integration and Use of Text, Image, Video, and Audio Corpora
, March 24–25
, Palo Alto, CA
.23.
Friedman
, J.
, Hastie
, T.
, and Tibshirani
, R.
(2001
). The Elements of Statistical Learning
(Springer
, New York
).24.
Fujinaga
, I.
, and MacMillan
, K.
(2000
). “Realtime recognition of orchestral instruments
,” in International Computer Music Association
(John Hopkins University
, Baltimore, MD
).25.
Guo
, G.
, and Li
, S. Z.
(2003
). “Content-based audio classification and retrieval by support vector machines
,” IEEE Trans. Neural Netw.
14
(1
), 209
–215
.26.
Han
, N. C.
, Muniandy
, S. V.
, and Dayou
, J.
(2011
). “Acoustic classification of Australian anurans based on hybrid spectral-entropy approach
,” Appl. Acoust.
72
(9
), 639
–645
.27.
Heck
, K. J.
(2003
). “Critical evaluation of nursery hypothesis for seagrasses
,” Mar. Ecol. Prog. Ser.
253
, 123
–136
.28.
Huang
, C.-J.
, Yang
, Y.-J.
, Yang
, D.-X.
, and Chen
, Y.-J.
(2009
). “Frog classification using machine learning techniques
,” Exp. Syst. Appl.
36
(2
), 3737
–3743
.29.
Lee
, C. H.
, Chou
, C. H.
, Han
, C. C.
, and Huang
, R. Z.
(2006
). “Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis
,” Pattern Recogn. Lett.
27
(2
), 93
–101
.30.
Lim
, T.
, Bae
, K.
, Hwang
, C.
, and Lee
, H.
(2007
). “Classification of underwater transient signals using MFCC feature vector
,” in Proceedings of the 9th International Symposium on Signal Processing and its Applications, ISSPA 2007
, February 12–15
, Sharjah, United Arab Emirates
, pp. 1
–4
.31.
Lossent
, J.
, Gervaise
, C.
, Iorio
, L.
, and Boissery
, P.
(2016
). “Cartographie de la biophonie des écosystèmes côtiers
” (“Biophony mapping of coastal ecosystems”), Traitement du Signal
33
(1
), 131
–151
.32.
Malfante
, M.
(2018
). Automatic Analysis Architecture (AAA)
, available at https://github.com/malfante/AAA.33.
Malfante
, M.
, Dalla Mura
, M.
, Mars
, J. I.
, Metaxian
, J. P.
, Macedo
, O.
, and Inza
, A.
(2017
). “Machine learning for volcano-seismic signals: Challenges and perspectives
,” IEEE Signal Process. Mag.
35
, 20
–30
.34.
Mann
, D. A.
, Hawkins
, A. D.
, and Jech
, J. M.
(2008
). Active and Passive Acoustics to Locate and Study Fish
(Springer
, New York
), p. 279
.35.
Márquez-Molina
, M.
, Sánchez-Fernández
, L. P.
, Suárez-Guerra
, S.
, and Sánchez-Pérez
, L. A.
(2014
). “Aircraft take-off noises classification based on human auditory's matched features extraction
,” Appl. Acoust.
84
, 83
–90
.36.
McCulloch
, W. S.
, and Pitts
, W.
(1943
). “A logical calculus of the ideas immanent in nervous activity
,” Bull Math. Biophys.
5
(4
), 115
–133
.37.
McIlraith
, A. L.
, and Card
, H. C.
(1997
). “Birdsong recognition using backpropagation and multivariate statistics
,” IEEE Trans. Signal Process.
45
(11
), 2740
–2748
.38.
Mitrovic
, D.
, Zeppelzauer
, M.
, and Breiteneder
, C.
(2006
). “Discrimination and retrieval of animal sounds
,” in Proceedings of the IEEE 2006 12th International Multi-Media Modelling Conference
, January 4–6
, Beijing, China
, p. 5
.39.
Noda
, J. J.
, Travieso
, C. M.
, and Sánchez-Rodríguez
, D.
(2016
). “Automatic taxonomic classification of fish based on their acoustic signals
,” Appl. Sci.
6
(12
), 443
.40.
Pace
, F.
, Benard
, F.
, Glotin
, H.
, Adam
, O.
, and White
, P.
(2010
). “Subunit definition and analysis for humpback whale call classification
,” Appl. Acoust.
71
(11
), 1107
–1112
.41.
Parmentier
, E.
, Vandewalle
, P.
, Frédérich
, B.
, and Fine
, M. L.
(2006
). “Sound production in two species of damselfishes (Pomacentridae): Plectroglyphidodon lacrymatus and Dascyllus aruanus
,” J. Fish Biol.
69
(2
), 491
–503
.42.
Sattar
, F.
, Cullis-Suzuki
, S.
, and Jin
, F.
(2016
). “Acoustic analysis of big ocean data to monitor fish sounds
,” Ecol. Inform.
34
, 102
–107
.43.
Thode
, A. M.
, Kim
, K. H.
, Blackwell
, S. B.
, Greene
, C. R.
, Jr., Nations
, C. S.
, McDonald
, T. L.
, and Macrander
, A. M.
(2012
). “Automated detection and localization of bowhead whale sounds in the presence of seismic airgun surveys
,” J. Acoust. Soc. Am.
131
(5
), 3726
–3747
.44.
Thorson
, R. F.
, and Fine
, M. L.
(2002
). “Crepuscular changes in emission rate and parameters of the boatwhistle advertisement call of the gulf toadfish, Opsanus beta
,” Environ. Biol. Fishes
63
(3
), 321
–331
.45.
Tucker
, S.
, and Brown
, G. J.
(2005
). “Classification of transient sonar sounds using perceptually motivated features
,” IEEE J. Oceanic Eng.
30
(3
), 588
–600
.46.
Tyagi
, H.
, Hegde
, R. M.
, Murthy
, H. A.
, and Prabhakar
, A.
(2006
). “Automatic identification of bird calls using spectral ensemble average voice prints
,” in Proceedings of the IEEE 2006 14th European Signal Processing Conference
, September 4–8
, Florence, Italy
, pp. 1
–5
.47.
Vieira
, M.
, Fonseca
, P. J.
, Amorim
, M. C. P.
, and Teixeira
, C. J.
(2015
). “Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish
,” J. Acoust. Soc. Am.
138
(6
), 3941
–3950
.48.
Wang
, S.
, and Zeng
, X.
(2014
). “Robust underwater noise targets classification using auditory inspired time-frequency analysis
,” Appl. Acoust.
78
, 68
–76
.49.
Wimmer
, J.
, Towsey
, M.
, Planitz
, B.
, Roe
, P.
, and Williamson
, I.
(2010
). “Scaling acoustic data analysis through collaboration and automation
,” in Proceedings of the 2010 IEEE Sixth International Conference on e-Science (e-Science)
, December 7–10
, Brisbane, Australia
, pp. 308
–315
.50.
Yu
, G.
, and Slotine
, J.-J.
(2009
). “Audio classification from time-frequency texture
,” in Proceedings of the IEEE International Conference Acoustics, Speech and Signal Processing, ICASSP 2009
, April 19–24
, Taipei, Taiwan
, pp. 1677
–1680
.51.
Zaugg
, S.
, Van Der Schaar
, M.
, Houégnigan
, L.
, Gervaise
, C.
, and André
, M.
(2010
). “Real-time acoustic classification of sperm whale clicks and shipping impulses from deep-sea observatories
,” Appl. Acoust.
71
(11
), 1011
–1019
.52.
Zheng
, F.
, Zhang
, G.
, and Song
, Z.
(2001
). “Comparison of different implementations of MFCC
,” J. Comput. Sci. Technol.
16
(6
), 582
–589
.© 2018 Acoustical Society of America.
2018
Acoustical Society of America
You do not currently have access to this content.
