Animal Sound Classification using Sequential Classifiers

Javier Romero, Amalia Luque, Alejandro Carrasco

2017

Abstract

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. But the recording, storage and further processing of a huge number of anuran's sounds, distributed in time and space, are required to obtain this indicator. It is therefore highly desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper five different classification methods are proposed, all of them based on the data mining domain, which try to take advantage of the sound sequential behaviour. Its definition and comparison is undertaken using several approaches. The sequential classifiers have revealed that they can obtain a better performance than their non-sequential counterpart. The sliding window with an underlying decision tree has reached the best results in our tests, even overwhelming the Hidden Markov Models usually employed in similar applications. A quite remarkable overall classification performance has been obtained, a result even more relevant considering the low quality of the analysed sounds.

References

  1. Aggarwal, C. C. (2007). Data streams: models and algorithms (Vol. 31). Springer Science and Business Media.
  2. Box, G. E., Jenkins, G. M., Reinsel, G. C. (2011). Time series analysis: forecasting and control (Vol. 734). John Wiley and Sons.
  3. Cover, T. M., Hart, P. E. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1), 21-27.
  4. Cristianini, N., Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press.
  5. Dietterich, T. G. (2002). Machine learning for sequential data: A review. In Structural, syntactic, and statistical pattern recognition (pp. 15-30). Springer Berlin Heidelberg.
  6. Dobson, A. J., Barnett, A. (2008). An introduction to generalized linear models. CRC press.
  7. Du, K. L., Swamy, M. N. S. (2013). Neural Networks and Statistical Learning. Springer Science and Business Media.
  8. Fonozoo.com (2016). Retrieved from http://www.fonozoo. com/
  9. Härdle, W. K., Simar, L. (2012). Applied multivariate statistical analysis. Springer Science and Business Media.
  10. Hastie, T., Tibshirani, R., Friedman, J. (2005). The elements of statistical learning: data mining, inference and prediction. Springer-Verlag.
  11. ISO (2001). ISO 15938-4:2001 (MPEG-7: Multimedia Content Description Interface), Part 4: Audio. ISO.
  12. Le Cam, L. M. (1979). Maximum likelihood: an introduction. Statistics Branch, Department of Mathematics, University of Maryland.
  13. Llusia, D., Márquez, R., Beltrán, J. F., Benitez, M., Do Amaral, J. P. (2013). Calling be-haviour under climate change: geographical and seasonal variation of calling temperatures in ectotherms. Global change biology, 19(9), 2655-2674.
  14. Luque, J., Larios, D. F., Personal, E., Barbancho, J., León, C. (2016). Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks. Sensors, 16(5), 717.
  15. Márquez, R., Bosch, J. (1995). Advertisement calls of the midwife toads Alytes (Amphibia, Anura, Discoglossidae) in continental Spain. Journal of Zoological Systematics and Evolutionary Research, 33(3-4), 185-192.
  16. Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
  17. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
  18. Rokach, Lior, Maimon, O. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc.
  19. Romero, J., Luque, A., Carrasco, A. (2016). Anuran sound classification using MPEG-7 frame descriptors. XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), 801-810.
  20. Schaidnagel, M., Connolly, T., Laux, F. (2014). Automated feature construction for classification of time ordered data sequences. International Journal on Advances in Software, 7(3 and 4), 632-641.
  21. Sokolova, M., Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427- 437.
  22. Wacker, A. G., Landgrebe, D. A. (1971). The minimum distance approach to classification. Purdue University. Information Note 100771.
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Paper Citation


in Harvard Style

Romero J., Luque A. and Carrasco A. (2017). Animal Sound Classification using Sequential Classifiers . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 242-247. DOI: 10.5220/0006246002420247


in Bibtex Style

@conference{biosignals17,
author={Javier Romero and Amalia Luque and Alejandro Carrasco},
title={Animal Sound Classification using Sequential Classifiers},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={242-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006246002420247},
isbn={978-989-758-212-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Animal Sound Classification using Sequential Classifiers
SN - 978-989-758-212-7
AU - Romero J.
AU - Luque A.
AU - Carrasco A.
PY - 2017
SP - 242
EP - 247
DO - 10.5220/0006246002420247