Automatic bird sound detection in long real-field recordings: Applications and tools

Applied Acoustics

Volumn 80, Issue , 2014, Pages 1-9

  Potamitis, Ilyas ^a   Ntalampiras, Stavros ^b   Jahn, Olaf ^c   Riede, Klaus ^c  

^a TECHNOLOGICAL EDUCATIONAL INSTITUTE OF CRETE   (Greece)

^b POLITECNICO DI MILANO   (Italy)

^c ZOOLOGISCHES FORSCHUNGSMUSEUM ALEXANDER KOENIG   (Germany)

Author keywords

Bird recognition; Birdsong detection; Computational ecology

Indexed keywords

ACOUSTIC DETECTION; BIRD SPECIES IDENTIFICATIONS; COMPARATIVE EXPERIMENTS; COMPUTATIONAL ECOLOGY; ENVIRONMENTAL INDICATORS; LONG-TERM RECORDING; SPECIES RECOGNITION; THREATENED SPECIES;

CONSERVATION; RESEARCH;

BIRDS;

EID: 84893310451     PISSN: 0003682X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apacoust.2014.01.001     Document Type: Article

References (41)

1. Wiggins S, Hildebrand J. High-frequency Acoustic Recording Package (HARP) for broad-band, long-term marine mammal monitoring. In: International symposium on underwater technology 2007 and international workshop on scientific use of submarine cables & related technologies 2007. Institute of Electrical and Electronics Engineers, Tokyo, Japan; 2007. p. 551-7. (Pubitemid 47468254)
2. K. Swiston, and D. Mennill Comparison of manual and automated methods for identifying target sounds in audio recordings of Pileated, Pale-billed, and putative Ivory-billed woodpeckers J Field Ornithol 80 2009 42 50
3. R. Buxton, and I. Jones Measuring nocturnal seabird activity and status using acoustic recording devices: applications for island restoration J Field Ornithol 83 1 2012 47 60
4. Slimani T, Beltrán Gala J, El Mouden E, Radi M, Marquez R. Recent discoveries and monitoring populations of the Moroccan Toad (Alytes maurus Pasteur & Bons, 1962). In: Proceedings of the XI Congresso Luso-Espanhol de Herpetología/Sevilha; 2010. p. 148.
5. K. Riede Monitoring biodiversity: analysis of Amazonian rainforest sounds Ambio 22 1993 546 548
6. Huntley B, Green R, Collingham Y, Willis SG. A climatic atlas of European breeding birds. Lynx Edicions; 2008. p. 521.
7. IUCN. IUCN Red List of threatened species, version 2011.2. < http://www.iucnredlist.org > [last accessed 15.08.13].
8. EC. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. < http://ec.europa.eu/ environment/nature/legislation/habitatsdirec-tive /index-en.htm > [last accessed 15/08/2013].
9. EC. Directive 2009/147/EC of the European Parliament and of the Council of 30 November 2009 on the conservation of wild birds. < http://ec.europa.eu/ environment/nature/legislation/birds-directive/index-en.htm > [last accessed 15/08/2013].
10. D. Blumstein, D. Mennill, P. Clemins, L. Girod, K. Yao, and G. Patricelli et al. Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus J Appl Ecol 48 2011 758 767
11. M. Alldredge, K. Pollock, and T. Simons Estimating detection probabilities from multiple-observer point counts Auk 123 4 2006 1172 1182 (Pubitemid 44655980)
12. Computational bioacoustics for assessing biodiversity. In: Frommolt Karl-Heinz, Bardeli Rolf, Clausen Michael, editors. Proceedings of the international expert meeting on IT-based detection of bioacoustical patterns. International Academy for Nature Conservation (INA), Isle of Vilm, Germany; 2008.
13. ICML 2013. In: Glotin H, Clark C, LeCun Y, Dugan P, Halkias X, Sueur J, editors. Workshop on machine learning for bioacoustics, June 16-21, 2013 - Atlanta, Georgia, United States.
14. WWF and ZSL editors. Living Planet Index, Factsheet 1.2.1. Biodiversity Indicator Partnership 2010. < http://static.zsl.org/files/1-2-1-living- planet-index-1062.pdf > [last accessed 15.08.13].
15. Clemins P, Trawicki M, Adi K, Tao J, Johnson M. Generalized perceptual features for vocalization analysis across multiple species. In: IEEE International conference on acoustics, speech and signal processing; 2006. p. 253-6.
16. P. Somervuo, A. Harma, and S. Fagerlund Parametric representations of bird sounds for automatic species recognition IEEE Trans ASSP 14 6 2006 2252 2263
17. A. Selin, J. Turunen, and J.T. Tanttu Wavelets in recognition of bird sounds EURASIP J Adv Signal Process 2007 [Article ID 51806]
18. T. Brandes Feature vector selection and use with hidden Markov models to identify frequency-modulated bioacoustic signals amidst noise IEEE Trans Audio Speech Lang Process 16 2008 1173 1180
19. J. Elizabeth A new perspective on acoustic individual recognition in animals with limited call sharing or changing repertoires Anim Behav 75 2008 1187 1194
20. P. Jancovic, and M. Kokuer Automatic detection and recognition of tonal bird sounds in noisy environments J Adv Signal Process 2011 2011 1 10
21. Cai J, Ee D, Pham B, Roe P, Zhang J. Sensor network for the monitoring of ecosystem: bird species recognition. In: 3rd International conference on intelligent sensors, sensor networks and information; 2008. p. 293-8.
22. J. Kogan, and D. Margoliash Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study J Acoust Soc Am 103 4 1998 2185 2196 (Pubitemid 28209044)
23. C. Kwan, K. Ho, G. Mei, Y. Li, Z. Ren, and R. Xu et al. An automated acoustic system to monitor and classify birds EURASIP J Appl Signal Process 2006 [Article ID 96706]
24. S. Fagerlund Bird species recognition using support vector machines EURASIP J Appl Signal Process 2007 [Article ID 38637]
25. V. Trifa, A. Kirschel, C.E. Taylor, and E.E. Vallejo Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models J Acoust Soc Am 123 4 2008 2424 2431 (Pubitemid 351503014)
26. R. Bardeli Similarity search in animal sound databases IEEE Trans Multimed 11 1 2009 68 76
27. Y. Ren, M. Johnson, P. Clemins, M. Darre, S. Glaeser, and T. Osiejuk A framework for bioacoustic vocalization analysis using hidden Markov models Algorithms 2 4 2009 1410 1428
28. Briggs F, Fern X, Irvine J. Multi-label classifier chains for bird sound. In: Proceedings of the 30th international conference on machine learning. Atlanta, Georgia, USA; 2013 [JMLR, W&CP volume 28].
29. F. Briggs, B. Lakshminarayanan, L. Neal, X. Fern, and R. Raich et al. Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach J Acoust Soc Am 131 2012 4640
30. D. Bardeli, Wolff, F. Kurth, M. Koch, K. Tauchert, and K. Frommolt Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring Pattern Recogn Lett 31 2009 1524 1534
31. P. Marler Bird calls: a cornucopia for communication P. Marler, H. Slabbekoorn, Nature's music: the science of birdsong 2004 Elsevier Academic Press New York (NY) 132 177 [chapter 5]
32. A. Doupe, and P. Kuhl Birdsong and human speech: common themes and mechanisms Annu Rev Neurosci 22 1999 567 631 (Pubitemid 29144898)
33. X. Huang, A. Acero, and H. Hon Spoken language processing 2001 Prentice Hall 1-1008
34. Baptista L, Kroodsma D. Avian bioacoustics, handbook of the birds of the world. In: del Hoyo J, Ellio A, Sargatal J, editors. Mousebirds to hornbills. Lynx Editions, Barcelona, Spain, vol. 6; 2001. p. 11-52.
35. Chu W, Blumstein D. Noise robust bird song detection using syllable pattern-based hidden Markov models. In: ICASSP, IEEE international conference on acoustic speech and signal processing; 2011. p. 345-8.
36. I. Cohen Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging IEEE Trans Speech Audio Process 11 5 2003 466 475
37. Eyben F, Wöllmer M, Schuller B. OpenSMILE - the munich versatile and fast open-source audio feature extractor. In: Proc ACM multimedia (MM). Firenze, Italy: ACM; 2010.
38. Hidden Markov model Toolkit. < http://htk.eng.cam.ac.uk/ > [date last viewed 15.08. 13].
39. Gales M, Young S. The application of hidden Markov models in speech recognition. In: Foundations and trends in signal processing, vol. 1, no. 3; 2007. p. 195-304 [c 2008 M. Gales and S. Young, doi: 10.1561/2000000004].
40. T. Kinnunen, and H. Li An overview of text-independent speaker recognition: from features to supervectors Speech Commun 52 1 2010 12 40
41. Catchpole C, Slater P. Bird songs: biological themes and variations. Cambridge; 2008.