Towards the automatic acoustical avian monitoring system

Autor

DOI:

https://doi.org/10.5604/01.3001.0010.7995

Słowa kluczowe:

bird voices recognition, bird song recognition, hidden Markov models, dynamic time warping, HFCC, MFCC

Abstrakt

One of the crucial aspects of the environmental protection is continuous monitoring of environment. Specific aspect is estimation of the bird species population. It is particularly important for bird species being in danger of extinction. Avian monitoring programs are time and money consuming actions which usually base on terrain expeditions. Certain remedy for this can be automatic acoustical avian monitoring system, described in the paper. Main components of the designed system are: digital audio recorder for bird voices acquisition, computer program automatically recognizing bird species by its signals emitted (voices or others) and object-relational database accessed via the Internet. Optional system components can be: digital camera and camcorder, bird attracting device, wireless data transmission module, power supply with solar panel, portable weather station. The system records bird voices and sends the recordings to the database. Recorded bird voices can be also provoked by the attracting device. Application of wireless data transmission module and power supply with solar panel allows long term operation of digital sound recorder in a hard accessible terrain. Recorded bird voices are analysed by the computer program and labelled with the automatically recognized bird species. Recognition accuracy of the program can be optionally enhanced by an expert system. Besides of labelled sound recordings, database can store also many other information like: photos and films accompanying recorded bird voices/ sounds, information about localization of observation/ recordings (GPS position, description of a place of an observation), information about bird features and behaviour, meteorological information, etc. Database on the base of geographical/ geological digital maps can generate actual maps of bird population (presence, number of individuals of each species). Moreover data-base can trigger alerts in case of rapidly decreasing bird population. It is also possible to obtain new knowledge about bird species with data mining methods. The paper presents collected data on observed bird species (audio recordings, photos and films) as well as results of experiments testing particular components of the automatic acoustical avian monitoring system.

Statystyka pobrań

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Directive 2009/147/EC of the European Parliament and of the Council of 30 November 2009 on the conservation of wild birds.   Google Scholar

Council Directive 92/43/EEC on the Conservation of natural habitats and of wild fauna and flora 1992.   Google Scholar

C. Bibby, N. Burgess, D. Hill, S. Mustoe, Bird Census Techniques. Elsevier 2007.   Google Scholar

A. Lisowska-Lis, R. Wielgat, D. Król, T. Potempa, Acoustical Bird Monitoring System – Recordigns Methodology and Species Chosen for Recordings. 5th International Conference on Interdisciplinarity in Education 6-8 May, 2010, Tallinn, Estonia.   Google Scholar

D. Król, R. Wielgat, T. Potempa, A. Lisowska-Lis, Acoustical Bird Monitoring System–Electronic Equipment. 5th International Conference on Interdisciplinarity in Education 6-8, May 2010, Tallinn, Estonia.   Google Scholar

T. Potempa, R. Wielgat, A. Lisowska-Lis, D. Król, Acoustical Bird Monitoring System–Data Base Aided Signal Recognition. 5th International Conference on Interdisciplinarity in Education 6-8 May, 2010. Tallinn, Estonia.   Google Scholar

H. Tyagi, R. M. Hegde, H. A. Murthy, A. Prabhakar Automatic identification of bird calls using spectral ensemble average voiceprints. Proceedings of the 13th European Signal Processing Conference (EUSIPCO ‘06), Florence, Italy, September 2006.   Google Scholar

Ch.-H. Chou, Ch.-H. Lee, H.-W Ni, Bird Species Recognition by Comparing the HMMs of the Syllables. ICICIC, pp.143, Second International Conference on Innovative Computing, Information and Control (ICICIC 2007), 2007.   Google Scholar

C. Kwan, K. Ho, An Automated Acoustic System to Monitor and Classify Birds. Bird Strike Committee Proceedings, 5th Joint Annual Meeting, Toronto, ONT, 2003.   Google Scholar

J.-M. Valin, F. Michaud, J. Rouat, D. Létourneau, Robust sound source localization using a microphone array on a mobile robot. in Proceedings International Conference on Intelligent Robots and Systems, 2003.   Google Scholar

D. Król, On superiority of Successive Approximation Register over Sigma Delta AD converter in standard audio measurements using Maximum Length Sequences. International Conference on Signals and Electronic Systems, ICSES’08, 14-17 September 2008, Kraków, Poland.   Google Scholar

D. Król, Choice of analog-to-digital converters for audio measurements using MLS algorithm. 15th European Signal Processing Conference, EUSIPCO 2007, 3-7 September 2007, Poznań, Poland.   Google Scholar

D. Król, R. Wielgat, T. Potempa, P. Świętojański, Analysis of Ultrasonic Components in Voices of Chosen Bird Species, Forum Acusticum 2011, 26 June-1 July 2011, Aalborg, Denmark.   Google Scholar

http://www.meteo.pl   Google Scholar

R. Wielgat, T. P. Zieliński, T. Potempa, A. Lisowska-Lis and D. Król, Signal Processing Algorithms, Architectures, Arrangements, and Applications, 2007, 129-134.   Google Scholar

Ch.-H. Lee, Y.-K. Lee, R.-Z Huang. Journal of Information Technology and Applications, 2006, 1, 17-23.   Google Scholar

T. Potempa, R. Wielgat, D. Król, P. Kozioł, A. Lisowska-Lis, Studia Informatica. Zeszyty Naukowe Politechniki Śląskiej, seria INFORMATYKA, 2010, 31 (2A), 375-391.   Google Scholar

http://www.birdsmond.pwsztar.edu.pl   Google Scholar

R. Wielgat, T. Potempa, P. Świętojański and D. Król, “On using prefiltration in HMM-based bird species recognition,” 2012 International Conference on Signals and Electronic Systems (ICSES), Wroclaw, 2012, 1-5.   Google Scholar

T. Fawcett, Pattern Recognition Letters, 2006, 27, 861-874.   Google Scholar

G. Mirzaei, M. M. Jamali, J. Ross, P. V. Gorsevski and V. P. Bingman, IEEE Sensors Journal, 2015, 15, 6625-6632   Google Scholar

C. wa Maina, D. Muchiri, P. Njoroge, Biodiversity Data Journal, 2016, 4, e9906.   Google Scholar

D. T. Blumstein, D. J. Mennill, P. Clemins, L. Girod, K. Yao, G. Patricelli, J. L. Deppe, A. H. Krakauer, C. Clark, K. A. Cortopassi, S. F. Hanser, B. McCowan, A. M. Ali, A. N. G. Kirschel, Journal of Applied Ecology, 2011, 48, 758-767.   Google Scholar

C. E. Sanders and D. J. Mennill, Ornithological Applications, 2016, 116, 371-383.   Google Scholar

M. A. Pryde, T. C. Greene, New Zealand Journal of Ecology, 2016, 40, 100-107.   Google Scholar

T. A. Mitchell et al. “Real-Time Bioacoustics Monitoring and Automated Species Identification.” Ed. Xiaolei Huang. PeerJ 1 (2013): e103. PMC. Web. 10 Apr. 2017.   Google Scholar

E. C. Leach, C. J. Burwell, L. A. Ashton, D. N. Jones, R. L. Kitching, (2016) Comparison of point counts and automated acoustic monitoring: detecting birds in a rainforest biodiversity survey. Emu 116, 305-309.   Google Scholar

A. Boulmaiz, D. Messadeg, N. Doghmane, et al., International Journal of Speech Technology, 2016, 19, 631-645.   Google Scholar

S. C. Prevost, Estimating Avian Populations with Passive Acoustic Technology and Song Behavior. Master’s Thesis, University of Tennessee, 2016.   Google Scholar

J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, et al., PLoS ONE, 2016, 11(11): e0166866.   Google Scholar

K.A. Williams, E.M. Adams, J. Fiely, D. Yates, P.B. Chilson, C. Kuster, D.C. Evers. 2013. Migratory Bird and Bat Monitoring in the Thousand Islands Region of New York State: Final Report, March 2013. Report to the U.S. Fish and Wildlife Service Columbus, Ohio Field Office. Report BRI 2013-11, Biodiversity Research Institute, Gorham, Maine.   Google Scholar

https://www.wildlifeacoustics.com   Google Scholar

https://www.fws.gov/radar/acoustic   Google Scholar

S. Duan, Automated species recognition in environmental recordings, PhD thesis, Queensland University of Technology, 2014.   Google Scholar

Opublikowane

2017-10-11

Jak cytować

Wielgat, R., Król, D., Potempa, T., Kozioł, P., & Lisowska-Lis, A. (2017). Towards the automatic acoustical avian monitoring system. Science, Technology and Innovation, 4(3), 17–38. https://doi.org/10.5604/01.3001.0010.7995