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A Semantic Sensing Information Representation for Bird Ecology

  • Rajani Reddy Gorrepati
  • Dong-Hwan Park
  • Do-Hyeun Kim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 181)

Abstract

Birds have become an increasing concern for ecological preservation and safety. This paper proposes architecture of semantic sensing information for bird acoustic data representation in bird ecological environment. This architecture provides various real-time sensing data such as bird calls using acoustic sensors in sensor networks. Information of this architecture supports to recognize bird calls, to identify birds, to classify species, and to track a bird behavior in bird ecological environment.

Keywords

Context-aware Bird ecology Semantic sensor information 

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References

  1. 1.
    Forren, J.F., Jaarsma, D.: Traffic monitoring by tire noise. In: Proc. IEEE Conf. on Intelligent Transportation System, Boston, pp. 177–182 (1997)Google Scholar
  2. 2.
    Anderson, S.E., Dave, A.S., Margoliash, D.: Template-based automatic recognition of birdsong syllables from continuous recordings. The Journal of the Acoustical Society of America 100(2), 1209–1219 (1996)CrossRefGoogle Scholar
  3. 3.
    Kogan, J.A., Margoliash, D.: Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study. The Journal of the Acoustical Society of America 103(4), 2185–2196 (1998)CrossRefGoogle Scholar
  4. 4.
    Kwan, C., Ho, K.C., Mei, G., et al.: An automated acoustic system to monitor and classify birds. EURASIP Journal on Applied Signal Processing 2006, Article ID 96706, 19 (2006)Google Scholar
  5. 5.
    Tyagi, H., Hegde, R.M., Murthy, H.A., Prabhakar, A.: Automatic identification of bird calls using spectral ensemble average voiceprints. In: Proceedings of the 13th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy (September 2006)Google Scholar
  6. 6.
    Vilches, E., Escobar, I.A., Vallejo, E.E., Taylor, C.E.: Data mining applied to acoustic bird species recognition. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, vol. 3, pp. 400–403 ( August 2006)Google Scholar
  7. 7.
    McIlraith, A.L., Card, H.C.: Birdsong recognition using back propagation and multivariate statistics. IEEE Trans. Signal Processing 45(11), 2740–2748 (1997); Daniel, S., Anind, K.D., Gregory, D.A.: The Context Toolkit: Aiding the Development of Context-enabled Applications. In: Proc. SIGCHI Conf. on Human Factors in Computing Systems, pp.434–441 (1999)Google Scholar
  8. 8.
    Wang, F.Y., Zeng, D., Yang, L.Q.: Smart cars on smart roads: an IEEE intelligent transportation systems society update. IEEE Perv. Comput. 5(4), 68–69 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Rajani Reddy Gorrepati
    • 1
  • Dong-Hwan Park
    • 2
  • Do-Hyeun Kim
    • 1
  1. 1.Dept. of Computer EngineeringJeju National UniversityJejuRepublic of Korea
  2. 2.Dept. of OfficeEletronics & Telecommunication Research InstituteDaejeonRepublic of Korea

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