INVESTIGATION ON THE POSSIBILITY OF USING ENTROPY APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF FROG SPECIES
DOI:
https://doi.org/10.11113/jt.v75.3699Keywords:
Bioacoustics signal, entropy, frogs sound analysis, pattern recognition, specisis identificationAbstract
Animal species identification based on their sound has received attentions from researchers. This is to establish fast and efficient identification method. Identification of frogs have been one of the examples where research activities have shown some progress. Mel Frequency Cepstrum Coefficient (MFCC) and Linear Predictive Coding (LPC), coupled with k-th Nearest Neighbor (k-NN) or Support Vector Machines (SVM) have been the favorate approachs used by researchers. Quite recently, a new classification and identification method of sound using entropy-based approach for species identification of Australian frogs was proposed. Shannon, Rènyi and Tsallis entropy were used as features of extraction for the purpose of pattern recognition. Result shows that the full entropy-based animal sound identification system has successfully identified most of the frog species used in this study. The overall classification accuracy is as high as 91% with two failures from nine samples at 70% and 40%, respectively. A comparative analysis highlights the advantages of full entropy approach over conventional frequency spectral and hybrid methods. This is shown especially in the running time of a computer that required to complete the species identifications process. The result presented in this paper indicates that full entropy-based method can be used for faster frog species identification.
References
Huang, C. J., Y. J. Yang, D. X. Yang, and Y. J. Chen. 2009. Frog Classification Using Machine Learning Techniques. Expert System Application. 36: 3737-3743.
Chesmore, E. D. 2004. Automated Bioacoustic Identification of Species. Anais DA Academia Brasileirade Ciências. 76(2): 435-440.
Fletcher, N. H. 2010. Acoustical Background to the Many Varieties of Birdsong. Acoustic Australia. 38(2): 59-62.
Chesmore, E. D. 2001. Application of Time Domain Signal Coding and Artificial Neural Networks to the Passive Acoustical Identification of Animals. Applied Acoustic. 62: 1359-1374.
Reby, D. , S. Lek, I. Dimopoulos, J. Joachim, J. Lauga, and S. Aulagnier. 1997. Artificial Neural Networks as a Classification Method in the Behavioural Sciences. Behavioural Processes. 40: 35-43.
Dietrich, C., G. Palm, and F. Schwenker. 2003. Decision Templates for the Classification of Bioacoustic Time Series. Information Fusion. 4: 101-109.
Kathryn, P. 1996. Where Have All the Frogs and Toads Gone. Bioscience 40(6): 2-4.
Beebee, T. J. C., and Griffiths, R. A. 2005. The Amphibian Decline Crisis: A Watershed for Conservation Biology? Biological Conservation. 125(3): 271-285.
Carey, C. 1997. Kathryn Phillips: 1995, Tracking the Vanishing Frogs, Penguin Books. Climatic Change. 37(3): 565-567.
Tan, W. C., Jaafar H., Ramli, D. A., Rosdi, B. A., and Shahrudin, S. 2014. Intelligent Frog Species Identification on Android Operating System. International Journal of Circuits, Systems and Signal Processing. 8: 137-148.
Yuan, C. L. T., and Ramli, D. A. 2013. Frog Sound Identification System for Frog Species Recognition. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 109: 41-50.
Ng, C. H., S. V. Muniandy, and Dayou, J. 2011. Acoustic Classification of Australian Anurans Based on Hybrid Spectral-Entropy Approach. Applied Acoustic. 72(9): 639-645.
Dayou, J., C. H. Ng, C. M. Ho, A. H. Ahmad, S. V. Muniandy, and M. N. Dalimin. 2011. Classification and Identification of Frog Sound Based on Entropy Approach. International Conference on Life Science and Technology (ICLST 2011). 7-9 Januari 2011. Mumbai, India.
Beck, C. 2009. Generalised Information and Entropy Measures In Physics. Contemporary Physics. 50(4): 495-510.
Buddle, C. M., J. Beguin, E. Bolduc, A. Mercado, and T. E. Sackett. 2005. The Importance and Use of Taxon Sampling Curves for Comparative Biodiversity Research with Forest Arthropod Assemblages. Canadian Entomologist. 137: 120-127.
Sueur, J., S. Pavoine, O. Hamerlynck, and S. Duvail. 2008. Rapid Acoustic Survey for Biodiversity Appraisal. PLoS ONE. 3(12): 4065.
Rényi, A. 1959. On the dimension and entropy of probability distributions. Acta Mathematica Academiae Scientiarum Hungaricae. 10: 193-215.
Rényi, A. 1961. On measures of entropy and information. Proceedings of the 4th Berkeley Symposium on Mathematics of Stats and Probability. University of California Press, Berkeley 1: 547-561.
Csisza´r, I. 1995. Generalized cutoff rates and Rényi’s information measures. IEEE Transactions on Information Theory. 41:26-34.
Vinga, S. and J. S. Almeida. 2004. Rényi continuous entropy of DNA sequences. Journal of Theoretical Biology. 231: 377-388.
Baraniuk, R., P. Flandrin, A. Janssen, and O. Michel. 2001. Measuring time-frequency information content using the Rényi entropies. IEEE Transactions on Information Theory. 47: 1391-1409.
Neemuchwala, H., A. Hero, and P. Carson. 2005. Image matching using alpha-entropy measures and entropic graphs. Signal Processing. 85: 277-296.
Song, K. S. 2001. Rényi information, loglikelihood and an intrinsic distribution measure. Journal of Statistical Planning and Inference. 93: 51-69.
Hart, P. E. 1975. Moment distributions in economics: an exposition. Journal of the Royal Statistical Society Series A. 138: 423-434.
Tsalis, C. 1988. Possible Generalization of Boltzmann-Gibbs Statistics. Journal of Statistical Physics. 52:479.
Yulmetyev, R. M., N. A. Emelyanova, and F. M. Gafarov. 2004. Dynamical Shannon entropy and information Tsallis entropy in complex systems. Journal of Physics A. 341: 649-676.
Hadzibeganovic, T. and S. A. Cannasc. 2009. A Tsallis' statistics based neural network model for novel word learning. Journal of Physics A.. 388: 732-746.
Rufiner, H. L., M. E. Torres, L. Gamero, and D. H. Milone. 2004. Introducing complexity measures in nonlinear physiological signals: application to robust speech recognition. Journal of Physics A.. 332: 496-508.
Burton, T. C. 1993. Family microhylidae. In: Glasby, C. G., Ross G. J. B, Beesley P. L. (editors). Fauna of Australia. 2A. Canberra: AGPS.
Fletcher, N. H. 2005. Acoustics system in biology: from insect to elephants. Acoustics Australia. 33(3): 83-88.
Fletcher, N. H. 1997. Sound in animal world. Acoustics Australia. 25(2): 69-74.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
