Classificação de Anuros Baseado em Vocalizações para Monitoramento Ambiental Pervasivo
Resumo
Neste trabalho desenvolvemos um sistema automático de classificação de anuros (rãs e sapos) baseado em suas vocalizações (coachar). Cada sinal bioacústico foi primeiramente segmentado em unidades menores chamadas “sílabas”, posteriormente uma etapa de pré-processamento, composta por um filtro de pre-ênfase e uma janela de Hamming, prepara o sinal para a extração das características. Foram usados os Coeficientes Mel (MFCCs) para representar os sinais bioacústicos e dois classificadores foram avaliados: kNN e SVM. Em nossos experimentos, alcançamos uma taxa de acerto de 98,97%, que demonstra que MFCC, geralmente utilizada em reconhecimento de fala, pode ser usada para o problema de reconhecimento de anuros. A taxa de reconhecimento de anuros foi melhorada em 16,09%, usando SVM e os MFCCs, comparada com os resultados encontrados na literatura.Referências
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Rabiner, L. R. and Schafer, R. W. (2007). Introduction to Digital Speech Processing. Foundations and Trends in Signal Processing, 1:1–194.
Riede, K. (1993). Monitoring biodiversity: analysis of Amazonian rainforest sounds. Ambio, 22(8):546–548.
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Bee, M. A. and Micheyl, C. (2008). The cocktail party problem: what is it? How can it be solved? And why should animal behaviorists study it? Journal of comparative psychology, 122(3):235–51.
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory., pages 144–152, New York, USA. ACM.
Cai, J., Ee, D., Pham, B., Roe, P., and Zhang, J. (2007). Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition. In 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, pages 293–298. IEEE.
Carey, C., Heyer, W. R., Wilkinson, J., a. Alford, R., Arntzen, J. W., Halliday, T., Hungerford, L., Lips, K. R., Middleton, E. M., a. Orchard, S., and a. Stanley Rand (2001). Amphibian Declines and Environmental Change: Use of Remote-Sensing Data to Identify Environmental Correlates. Conservation Biology, 15(4):903–913.
Clemins, P. J. (2005). Automatic classification of animal vocalizations. PhD thesis, Marquette University, Wisconsin.
Collins, J. P. and Storfer, A. (2003). Global amphibian declines: sorting the hypotheses. Diversity and Distributions, 9:89–98.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Transactions on Information Theory, IEEE, 13(1):21–27.
Davis, S. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4):357–366.
Deller, J., Hansen, J., and Proakis, J. (1993). Discrete-time processing of speech signals, volume 1. IEEE.
Haddad, C. (2005). Guia Sonoro dos Anfíbios Anuros da Mata Atlântica.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA Data Mining Software: An Update.
Harma, A. (2003). Automatic identification of bird species based on sinusoidal modeling of syllables. In IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 545–548. IEEE.
Hu, W., Tran, V. N., Bulusu, N., Chou, C. T., Jha, S., and Taylor, A. (2005). The design and evaluation of a hybrid sensor network for cane-toad monitoring. In Fourth International Symposium on Information Processing in Sensor Networks., pages 503–508.
Huang, C.-J., Yang, Y.-J., Yang, D.-X., and Chen, Y.-J. (2009). Frog classification using machine learning techniques. Expert Systems with Applications, 36(2):3737–3743.
Márquez, R., Riva, I., Matheu, B., and Matheu, E. (2002). Sounds of Frogs and Toads of Bolivia.
Marsland, S. (2009). Machine Learning: an algorithmic perspective., volume 1. CRC Press, Palmerston North, New Zealand.
Quinlan, J. R. (1993). C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Rabiner, L. R. and Schafer, R. W. (2007). Introduction to Digital Speech Processing. Foundations and Trends in Signal Processing, 1:1–194.
Riede, K. (1993). Monitoring biodiversity: analysis of Amazonian rainforest sounds. Ambio, 22(8):546–548.
Taylor, A., Watson, G., Grigg, G., and McCallum, H. (1996). Monitoring Frog Communities: An Application of Machine Learning. Proceedings of the 8th Innovative Applications of Artificial Conference.
Publicado
19/07/2011
Como Citar
COLONNA, Juan Gabriel; NAKAMURA, Eduardo Freire; SANTOS, Eulanda Miranda dos.
Classificação de Anuros Baseado em Vocalizações para Monitoramento Ambiental Pervasivo. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 3. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1093-1102.
ISSN 2595-6183.
