Advertisement

Identification of Animal Species from Their Sounds

  • Gavril-Petre PopEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 71)

Abstract

Identification of animal species based on their sounds has already proven to be useful in biodiversity assessment. In this paper we explore the use of a combined Teager—cepstral—TESPAR (Time Encoded Signal Processing And Recognition) analysis to discriminate between different animal species. Our experiments using this approach together with classification techniques shows that TESPAR S-matrices of Teager cepstral coefficients along with some additional features can be successfully used to discriminate between different animal species, even in the conditions of small training sets.

Keywords

Animal species identification Cepstral coefficients TESPAR analysis Teager energy operator Machine learning 

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Lee, C.-H., Lee, Y.-K., Huang, R.-Z.: Automatic recognition of bird songs using cepstral coefficients. J. Inf. Technol. Appl 1(1), 17–23 (2006)Google Scholar
  2. 2.
    Chesmore, E.D.: Automated bioacoustic identification of species. An. Acad. Bras. Ciênc. 76(2), 435–440 (2004)CrossRefGoogle Scholar
  3. 3.
    Kaiser, J.F.: Some useful properties of Teager’s energy operators. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp 149–152 (1993)Google Scholar
  4. 4.
    Patil, H.A., Basu, T.K.: Identifying perceptually similar languages using teager energy based cepstrum. Eng. Lett. 16(1), 151–159 (2008)Google Scholar
  5. 5.
    King, R.A., Phipps, T.C.: Shannon, TESPAR and approximation strategies. In: ICSPAT 98, vol. 2, pp. 1204–1212. Toronto, Canada, Sept. 1998 (1998)Google Scholar
  6. 6.
    Lupu, E., Emerich, S., Beaufort, F.: On-line signature recognition using a global features fusion approach. Acta Tech. Napoc., Electron. Telecommun. 50, 13–20 (2009)Google Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Communications DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

Personalised recommendations