Signal classification by similarity and feature extraction with application in automatic insect identification
Resumo
Insects have a strong relationship with the human-beings. For example, some species of mosquito transmit diseases that kill millions of people around the world. At the same time, the presence of certain insects is essential for the ecological balance and food production. For this reason, we are developing a novel sensor as a tool to efficiently control disease vectors and agricultural pests without harming other species. In this paper, we demonstrate how we overtook the most important challenge to make this sensor practical: the creation of accurate classification systems. Despite the short duration and the very simple structure of the signal, we managed to successfully identify relevant features using speech and audio analysis techniques. We show that we can achieve an accuracy of 98% in the task of disease vector mosquitoes identification.
Referências
Batista, G. E. A. P. A., Silva, D. F., and Prati, R. C. (2012). An experimental design to evaluate class imbalance treatment methods. In International Conference on Machine Learning and Applications, pages 95–101.
Benedict, M. Q. and Robinson, A. S. (2003). The first releases of transgenic mosquitoes: an argument for the sterile insect technique. Trends in parasitology, 19(8):349–355.
Campana, B. J. and Keogh, E. J. (2010). A compression-based distance measure for texture. Statistical Analysis and Data Mining, 3(6):381–398.
Itakura, F. (1975). Line spectrum representation of linear predictor coefficients of speech signals. The Journal of the Acoustical Society of America, 57:S35.
Prati, R. C., Silva, D. F., and Batista, G. E. A. P. A. (2014). Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Journal of Knowledge and Information Systems, pages 1–24.
Silva, D. F. (2014). Classificação de séries temporais por similaridade e extração de atributos com aplicação na identificação automática de insetos. Master’s thesis, Universidade de São Paulo.
Silva, D. F., Batista, G. E. A. P. A., Keogh, E. J., and Mafra-Neto, A. (2011). Resultados preliminares na classificação de insetos utilizando sensores ópticos. In Encontro Nacional de Inteligˆencia Artificial, pages 1–12.
Silva, D. F., Papadopoulos, H., Batista, G. E. A. P. A., and Ellis, D. P.W. (2013a). A video compression-based approach to measure music structural similarity. In International Society for Music Information Retrieval Conference, pages 95–100.
Silva, D. F., Souza, V. M. A., and Batista, G. E. A. P. A. (2013b). Time series classification using compression distance of recurrence plots. In International Conference on Data Mining, pages 687–696.
Silva, D. F., Souza, V. M. A., and Batista, G. E. A. P. A. (2014). Exploring low cost laser sensors to identify flying insect species. Journal of Intelligent and Robotic Systems, pages 1–18.
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., and Giusti, R. (2012). Spoken digit recognition in Portuguese using Line Spectral Frequencies. In Ibero-American Conference on Artificial Intelligence, pages 241–250.
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., Keogh, E. J., and Ellis, D. P. W. (2013c). Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. In International Conference on Machine Learning and Applications, pages 99–104.
Silva, D. F., Souza, V. M. A. d., and Batista, G. E. A. P. A. (2013d). A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. Acta Scientiarum. Technology, 35(4):621–628.
Souza, V. M. A., Silva, D. F., and Batista, G. E. A. P. A. (2013a). Classification of data streams applied to insect recognition: Initial results. In Brazilian Conference on Intelligent Systems, pages 76–81.
Souza, V. M. A., Silva, D. F., and Batista, G. E. A. P. A. (2014). Extracting texture features for time series classification. In International Conference on Pattern Recognition, pages 1425–1430.
Souza, V. M. A., Silva, D. F., Garcia, P. R., and Batista, G. E. A. P. A. (2013b). Avaliação de classificadores para o reconhecimento automático de insetos. In Encontro Nacional de Inteligˆencia Artificial e Computacional, pages 1–12.
Walker, K. (2002). A review of control methods for African malaria vector. Technical Report 108, Bureau for Global Health.
W.H.O. (2012). The world malaria report. Technical report, World Health Organization.
Benedict, M. Q. and Robinson, A. S. (2003). The first releases of transgenic mosquitoes: an argument for the sterile insect technique. Trends in parasitology, 19(8):349–355.
Campana, B. J. and Keogh, E. J. (2010). A compression-based distance measure for texture. Statistical Analysis and Data Mining, 3(6):381–398.
Itakura, F. (1975). Line spectrum representation of linear predictor coefficients of speech signals. The Journal of the Acoustical Society of America, 57:S35.
Prati, R. C., Silva, D. F., and Batista, G. E. A. P. A. (2014). Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Journal of Knowledge and Information Systems, pages 1–24.
Silva, D. F. (2014). Classificação de séries temporais por similaridade e extração de atributos com aplicação na identificação automática de insetos. Master’s thesis, Universidade de São Paulo.
Silva, D. F., Batista, G. E. A. P. A., Keogh, E. J., and Mafra-Neto, A. (2011). Resultados preliminares na classificação de insetos utilizando sensores ópticos. In Encontro Nacional de Inteligˆencia Artificial, pages 1–12.
Silva, D. F., Papadopoulos, H., Batista, G. E. A. P. A., and Ellis, D. P.W. (2013a). A video compression-based approach to measure music structural similarity. In International Society for Music Information Retrieval Conference, pages 95–100.
Silva, D. F., Souza, V. M. A., and Batista, G. E. A. P. A. (2013b). Time series classification using compression distance of recurrence plots. In International Conference on Data Mining, pages 687–696.
Silva, D. F., Souza, V. M. A., and Batista, G. E. A. P. A. (2014). Exploring low cost laser sensors to identify flying insect species. Journal of Intelligent and Robotic Systems, pages 1–18.
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., and Giusti, R. (2012). Spoken digit recognition in Portuguese using Line Spectral Frequencies. In Ibero-American Conference on Artificial Intelligence, pages 241–250.
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., Keogh, E. J., and Ellis, D. P. W. (2013c). Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. In International Conference on Machine Learning and Applications, pages 99–104.
Silva, D. F., Souza, V. M. A. d., and Batista, G. E. A. P. A. (2013d). A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. Acta Scientiarum. Technology, 35(4):621–628.
Souza, V. M. A., Silva, D. F., and Batista, G. E. A. P. A. (2013a). Classification of data streams applied to insect recognition: Initial results. In Brazilian Conference on Intelligent Systems, pages 76–81.
Souza, V. M. A., Silva, D. F., and Batista, G. E. A. P. A. (2014). Extracting texture features for time series classification. In International Conference on Pattern Recognition, pages 1425–1430.
Souza, V. M. A., Silva, D. F., Garcia, P. R., and Batista, G. E. A. P. A. (2013b). Avaliação de classificadores para o reconhecimento automático de insetos. In Encontro Nacional de Inteligˆencia Artificial e Computacional, pages 1–12.
Walker, K. (2002). A review of control methods for African malaria vector. Technical Report 108, Bureau for Global Health.
W.H.O. (2012). The world malaria report. Technical report, World Health Organization.
Publicado
20/07/2015
Como Citar
SILVA, Diego; BATISTA, Gustavo.
Signal classification by similarity and feature extraction with application in automatic insect identification. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 28. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 79-84.
ISSN 2763-8820.
DOI: https://doi.org/10.5753/ctd.2015.10006.
