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Wavelets in Recognition of Bird Sounds

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 051806 (2006) Cite this article

Abstract

This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmonic sounds. Inharmonic sounds are not well matched to the conventional spectral analysis methods, because the spectral domain does not include any visible trajectories that computer can track and identify. Thus, the wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyze signals which contain discontinuities and sharp spikes. The shift invariant feature vectors calculated from the wavelet coefficients were used as inputs of two neural networks: the unsupervised self-organizing map (SOM) and the supervised multilayer perceptron (MLP). The results were encouraging: the SOM network recognized 78% and the MLP network 96% of the test sounds correctly.

References

  1. Catchpole CK, Slater PJB: Bird Song: Biological Themes and Variations. Cambridge University Press, Cambridge, UK; 1995.

    Google Scholar 

  2. Kroodsma DE: The Singing Life of Birds: The Art and Science of Listening Birdsong. Houghton Miflin, Boston, Mass, USA; 2005.

    Google Scholar 

  3. Greenewalt CH: Bird Song: Acoustics and Physiology. Smithsonian Institution Press, Washington, DC, USA; 1968.

    Google Scholar 

  4. Zollinger SA, Riede T, Suthers RA: Production of nonlinear phenomena in the Northern Mockingbirds ( Minus polyglottos ). Proceedings of the 1st International Conference on Acoustic Communication by Animals, July 2003, College Park, Md, USA 283–284.

    Google Scholar 

  5. Suthers RA, Beckers G, Zollinger SA, Vallet E, Kreuzer M: Mechanisms of vocal complexity in birds. Proceedings of the 1st International Conference on Acoustic Communication by Animals, July 2003, College Park, Md, USA 237–238.

    Google Scholar 

  6. Bradbury JW: Parrots and technology. Proceedings of the 1st International Conference on Acoustic Communication by Animals, July 2003, College Park, Md, USA 29–30.

    Google Scholar 

  7. Baker MC, Logue DM: Population differentiation in a complex bird sound: a comparison of three bioacoustical analysis procedures. Ethology 2003,109(3):223–242. 10.1046/j.1439-0310.2003.00866.x

    Article  Google Scholar 

  8. Groth JG: Call matching and positive assortative mating in red crossbills. The Auk 1993,110(2):398–401.

    Google Scholar 

  9. Robb MS: Introduction to vocalizations of crossbills in Northwestern Europe. Dutch Birding 2000,22(2):61–107.

    Google Scholar 

  10. Deecke VB, Janik VM: Automated categorization of bioacoustic signals: avoiding perceptual pitfalls. Journal of the Acoustical Society of America 2006,119(1):645–653. 10.1121/1.2139067

    Article  Google Scholar 

  11. Elowson AM, Hailman JP: Analysis of complex variation: dichotomous sorting of predator-elicited calls of the Florida scrub jay. Bioacoustics 1991,3(4):295–320.

    Article  Google Scholar 

  12. Groth JG: Resolution of cryptic species in appalachian red crossbills. The Condor 1988,90(4):745–760. 10.2307/1368832

    Article  Google Scholar 

  13. Lovell SF, Lein MR: Song variation in a population of Alder Flycatchers. Journal of Field Ornithology 2004,75(2):146–151.

    Article  Google Scholar 

  14. Härmä A: Automatic identification of bird species based on sinusoidal modelling of syllables. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 545–548.

    Google Scholar 

  15. Härmä A, Somervuo P: Classification of the harmonic structure in bird vocalization. Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 5: 701–704.

    Google Scholar 

  16. Mesgarani N, Shamma S: Bird call classification using multiresolution spectrotemporal auditory model. Proceedings of the 1st International Conference on Acoustic Communication by Animals, July 2003, College Park, Md, USA 155–156.

    Google Scholar 

  17. Tanttu JT, Turunen J, Selin A, Ojanen M: Automatic feature extraction and classification of crossbill ( Loxia spp. ) flight calls. Bioacoustics 2006,15(3):251–269.

    Article  Google Scholar 

  18. Somervuo P, Härmä A: Bird song recognition based on syllable pair histograms. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 5: 825–828.

    Google Scholar 

  19. Fagerlund S, Härmä A: Parametrization of inharmonic bird sounds for automatic recognition. proceedings of the 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, Turkey Proceedings on CD-ROM

    Google Scholar 

  20. Rioul O, Vetterli M: Wavelets and signal processing. IEEE Signal Processing Magazine 1991,8(4):14–38. 10.1109/79.91217

    Article  Google Scholar 

  21. Soman AK, Vaidyanathan PP: Paraunitary filter banks and wavelet packets. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), March 1992, San Francisco, Calif, USA 397–400.

    Google Scholar 

  22. Pittner S, Kamarthi SV: Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 1999,21(1):83–88. 10.1109/34.745739

    Article  Google Scholar 

  23. Learned R: Wavelet packet based transient signal classification, M.S. thesis.

  24. Phelps SM, Ryan MJ: Neural networks predict response biases of female tungara frogs. Proceedings of the Royal Society—Biological Sciences (Series B) 1998,265(1393):279–285. 10.1098/rspb.1998.0293

    Article  Google Scholar 

  25. Deecke VB, Ford JKB, Spong P: Quantifying complex patterns of bioacoustic variation: use of a neural network to compare killer whale (Orcinus orca) dialects. The Journal of the Acoustical Society of America 1999,105(4):2499–2507. 10.1121/1.426853

    Article  Google Scholar 

  26. Placer J, Slobodchikoff CN: A fuzzy-neural system for identification of species-specific alarm calls of Gunnison's prairie dogs. Behavioural Processes 2000,52(1):1–9. 10.1016/S0376-6357(00)00105-4

    Article  Google Scholar 

  27. Thorn A: Artificial neural networks for vocal repertoire analysis. Proceedings of the 1st International Conference on Acoustic Communication by Animals, July 2003, College Park, Md, USA 245–246.

    Google Scholar 

  28. McIlraith AL, Card HC: Birdsong recognition using backpropagation and multivariate statistics. IEEE Transactions on Signal Processing 1997,45(11):2740–2748. 10.1109/78.650100

    Article  Google Scholar 

  29. Terry AMR, McGregor PK: Census and monitoring based on individually identifiable vocalizations: the role of neural networks. Animal Conservation 2002,5(2):103–111. 10.1017/S1367943002002147

    Article  Google Scholar 

  30. Somervuo P, Härmä A: Analyzing bird song syllables on the self-organizing map. Proceedings of the Workshop on Self-Organizing Maps (WSOM '03), September 2003, Hibikino, Japan Proceedings on CD-ROM

    Google Scholar 

  31. Boggess A, Narcowich FJ: A First Course in Wavelets with Fourier Analysis. Prentice-Hall, Upper Saddle River, NJ, USA; 2001.

    MATH  Google Scholar 

  32. Daubechies I: Ten Lectures on Wavelets. SIAM, Philadelphia, Pa, USA; 1992.

    Book  Google Scholar 

  33. Akansu AN, Haddad RA: Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets. Academic Press, Boston, Mass, USA; 1992.

    MATH  Google Scholar 

  34. Misiti M, Misiti Y, Oppenheim G, Poggi J-M: Wavelet Toolbox for Use with Matlab. MathWorks, Natick, Mass, USA; 2000.

    MATH  Google Scholar 

  35. Kohonen T: Self-Organizing Maps. Springer, Berlin, Germany; 2001.

    Book  Google Scholar 

  36. Haykin S: Neural Networks: A Comprehensive Foundation. Macmillan College, New York, NY, USA; 1994.

    MATH  Google Scholar 

  37. MathWorks : Matlab Software Homepage. June 2005, https://doi.org/www.mathworks.com

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Authors and Affiliations

  1. Department of Information Technology, Tampere University of Technology, P.O. Box 300, Pori, 28101, Finland

    Arja Selin, Jari Turunen & Juha T. Tanttu

Authors
  1. Arja Selin
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  2. Jari Turunen
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  3. Juha T. Tanttu
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Correspondence to Arja Selin.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Selin, A., Turunen, J. & Tanttu, J.T. Wavelets in Recognition of Bird Sounds. EURASIP J. Adv. Signal Process. 2007, 051806 (2006). https://doi.org/10.1155/2007/51806

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