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Identification of Birds Using Spectrogram Image Processing and Artificial Neural Network Classifiers Cover

Identification of Birds Using Spectrogram Image Processing and Artificial Neural Network Classifiers

Open Access
|Dec 2020

References

  1. [1] S. Arja, J. Turunen, J. Tanttu “Wavelets in Recognition of Bird Sounds” EURASIP Journal on Advances in Signal Processing. 2007. DOI 10.1155/2007/51806.
  2. [2] C. K. Catchpole and P. J. B. Slater, “Bird Song: Biological Themes and Variations”, Cambridge University Press, Cambridge, UK, 1995.
  3. [3] D. E. Kroodsma, “The Singing Life of Birds: The Art and Science of Listening Birdsong”, Houghton Miflin, Boston, Mass, USA, 2005.
  4. [4] C. H. Greenewalt, “Bird Song: Acoustics and Physiology”, Smithsonian Institution Press, Washington, DC, USA, 1968.
  5. [5] W. Haruka, “The Development of Birdsong” Department of Biology, University of Western Ontario 2010 Nature Education
  6. [6] M. C. Baker and D. M. Logue, “Population differentiation in a complex bird sound: a comparison of three bioacoustical analysis procedures,” Ethology, vol. 109, no. 3, pp. 223–242, 2003.10.1046/j.1439-0310.2003.00866.x
  7. [7] J. G. Groth, “Call matching and positive assortative mating in red crossbills,” The Auk, vol. 110, no. 2, pp. 398–401, 1993.
  8. [8] V. B. Deecke and V. M. Janik, “Automated categorization of bioacoustic signals: avoiding perceptual pitfalls,” Journal of the Acoustical Society of America, vol. 119, no. 1, pp. 645–653, 2006.10.1121/1.213906716454318
  9. [9] A. L. McIlraith and H. C. Card, “Birdsong recognition using backpropagation and multivariate statistics,” IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2740–2748, 1997.
  10. [10] A. M. R. Terry and P. K. McGregor, “Census and monitoring based on individually identifiable vocalizations: the role of neural networks,” Animal Conservation, vol. 5, no. 2, pp. 103–111, 2002.10.1017/S1367943002002147
  11. [11] A. M. Elowson and J. P. Hailman, “Analysis of complex variation: dichotomous sorting of predator-elicited calls of the Florida scrub jay,” Bioacoustics, vol. 3, no. 4, pp. 295–320, 1991.10.1080/09524622.1991.9753191
  12. [12] J. G. Groth, “Resolution of cryptic species in appalachian red crossbills,” The Condor, vol. 90, no. 4, pp. 745–760, 1988.10.2307/1368832
  13. [13] S. F. Lovell and M. R. Lein, “Song variation in a population of Alder Flycatchers”
  14. [14] J. T. Tanttu, J. Turunen, A. Selin, and M. Ojanen, “Automatic feature extraction and classification of crossbill (Loxia spp.) flight calls,” Bioacoustics, vol. 15, no. 3, pp. 251–269, 2006.10.1080/09524622.2006.9753553
  15. [15] S. Fagerlund, “Automatic Recognition of Bird Species by Their Sounds” Masters Thesis, HUT, Laboratory of Acoustics and Audio Signal Processing, (Espoo), 2004
  16. [16] Ilyas Potamitis “Deep learning for detection of bird vocalisations”, arXiv: 1609.08408, 2016
  17. [17] L. Fanioudakis, I. Potamitis “Deep networks tag the location of bird vocalisations on audio spectrograms”, arXiv: 1711.04347, 2017
  18. [18] link, accessed on 11.06.2020. - https://www.xeno-canto.org/
DOI: https://doi.org/10.2478/aucts-2020-0002 | Journal eISSN: 2668-6449 | Journal ISSN: 1583-7149
Language: English
Page range: 12 - 16
Published on: Dec 31, 2020
Published by: Lucian Blaga University of Sibiu
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Andrei-Ionuț Cheroiu, Mihai Neghină, published by Lucian Blaga University of Sibiu
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.