Advertisement

Automatic Recognition of Bird Species Using Human Factor Cepstral Coefficients

  • Arti V. BangEmail author
  • Priti P. Rege
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)

Abstract

Identification of bird species based on their song is very important task from biodiversity point of view. In order to develop an automatic system of recognition of bird species, a system using signal processing and pattern recognition techniques has gained huge importance. In this paper, we compare the performance of mel frequency cepstral coefficients and human factor cepstral coefficients combined with time- and frequency-based features. Gaussian mixture models have been used for developing feature models, and maximum likelihood estimation is used for classification. Further, selective features have been used in order to increase the performance of the system. With the proposed method, a maximum accuracy of 97.72% has been achieved for a data set of ten bird species.

Keywords

Bird species recognition Mel frequency cepstral coefficients Human factor cepstral coefficients Gaussian mixture modeling 

References

  1. 1.
    Brandes, T.S.: Automated sound recording and analysis techniques for bird surveys and conservation. Bird Conserv. Int. 18, S163–S173 (2008). doi: https://doi.org/10.1017/S0959270908000415CrossRefGoogle Scholar
  2. 2.
    Kumar, A.: Acoustic communication in birds, differences in songs and calls, their production and biological significance. Resonance 6, 44–54 (2003)CrossRefGoogle Scholar
  3. 3.
    Anderson, S.E., Dave, A.S., Margoliash, D.: Template-based Automatic recognition of birdsong syllables from continuous recordings. J. Acoust. Soc. Am. 100(2), 1209–1219 (1996)CrossRefGoogle Scholar
  4. 4.
    Kogan, J., Margoliash, D.: Automated recognition of bird song elements from continuous rcordings using dynamic time warping and hidden Markov models: a comparative study. J. Acoust. Soc. Am. 103(4), 2187–2196 (1998)CrossRefGoogle Scholar
  5. 5.
    Kwan, C., Ho, K., Mei, G., Li, Y, Ren, Z.: An automated acoustic system to monitor and classify birds. EURASIP J. Appl. Signal Process. 2006, 1–19 (2006). Article ID 96706Google Scholar
  6. 6.
    Lee, C.H., Han, C.H.: Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients. IEEE Trans. Speech Audio Process. 16(8), 1541–1550 (2008)CrossRefGoogle Scholar
  7. 7.
    Trifa, V.M., Kirschel, A., Taylor, C.E.: Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. J. Acoust. Soc. Am. 103(4), 2424–2431 (2008)CrossRefGoogle Scholar
  8. 8.
    Castano, G.V., Rodriguez, G., Castillo, J., Lu, K., Rios, A., Bird, F.: A Framework for bioacoustical species classifications in a versatile service-oriented wireless mesh networks. In: 18th European Signal Processing Conference, Aug 23–27 (2010)Google Scholar
  9. 9.
    Quian, K., Zhiang, Z., Ringeval, F., Schuller, B.: Bird sounds classification by large scale acoustic features and extreme learning machine. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2015)Google Scholar
  10. 10.
    Somervuo, P., Harma, A., Fagurland, S.: Parametric representations of bird sounds for automatic species recognition. IEEE Trans. Audio Speech Lang. Process. 14(6), 2252–2263 (2006)CrossRefGoogle Scholar
  11. 11.
    Fagurland, S.: Bird species recognition using support vector machine. EURASIP J. Adv. Signal Process. Article ID 38637, 8 p (2006). doi: https://doi.org/10.1155/2007/38637
  12. 12.
    Briggs, F., Laxminarayanan, B., Neal, L., Fern, X.Z., Raich, R.: Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. J. Acoust. Soc. Am. 131, 4640–4650 (2012)CrossRefGoogle Scholar
  13. 13.
    Mporas, I., Ganchev, T., Kocsis, O., Fakotakis, N., Jahn, O., Riede, K.: Automated acoustic classification of bird species from real-field recordings. In: 24th IEEE International Conference on tools with Artificial Intelligence (2012). doi: https://doi.org/10.1109/ICTAI.2012.110
  14. 14.
    Selin, A., Turunen, J., Tanttu, T.: Wavelets in recognition of bird sounds. EURASIP J. Adv. Signal Process. Article ID 51806, 9 p (2007). doi: https://doi.org/10.1155/2007/51806
  15. 15.
    Bang, A.V., Rege, P.P.: Classification of bird species based on bioacoustics. Int. J. Adv. Comput. Sci. Appl. 4(1), 184–188 (2014)Google Scholar
  16. 16.
    Jancovic, P.: Automatic detection and recognition of tonal bird sounds in noisy environments. EURASIP J. Adv. Signal Process. (2011). doi: https://doi.org/10.1155/2011/982936CrossRefGoogle Scholar
  17. 17.
    Vilches, E., Escobar, I.A., Vallejo,E.E.,Taylor, C. E.: Data mining applied to acoustic bird species recognition. In: 18th International Conference in Pattern Recognition (2006). doi:  https://doi.org/10.1109/ICPR.2006.426
  18. 18.
    Skowronski, M., Harris, J.: Improving the filter bank of a classic speech feature extraction algorithm. In: IEEE Internatinal Symposium on Circuits and Systems, Bangkok, Thailand, vol. IV, pp. 281–284, May 25–28 (2003)Google Scholar
  19. 19.
    Ganchev, T., Fakotakis, N., Kokkinakis, G.: Comparative evaluation of various MFCC implementations on the speaker verification task. In: Proceedings of the SPECOM, pp. 191–194 (2005)Google Scholar
  20. 20.
    Moore, B.C., Glasberg, B.R.: Suggested formulae for calculating auditory-filter bandwidths and excitation patterns. J. Acoust. Soc. Am. 74(3), 750–753 (1983)CrossRefGoogle Scholar
  21. 21.
    Bouman, C.A., Shapiro, M., Cook, G.W., Atkins, C.B., Cheng, H.: Cluster: an unsupervised algorithm for modeling gaussian mixtures. http://dynamo.ecn.purdue.edu/∼bouman/software/cluster (1998)
  22. 22.
    Boll, S.F.: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Process. 27, 113–120 (1979)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and TelecommunicationCollege of EngineeringPuneIndia

Personalised recommendations