تشخیص اردک های بیمار بر اساس صدای آنها و به کمک روش هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشگاه تربیت مدرس

2 دانشجوی دکتری

چکیده

در این مقاله روشی هوشمند به منظور طبقه­بندی اردک­های سالم و بیمار بر اساس صدای منتشره از آن­ها طراحی و به کار گرفته شده است. بدین منظور ابتدا پرندگان بر اساس وضعیت سلامتی به دو طبقه­ی سالم و بیمار تقسیم و صدای هر یک توسط یک میکروفن ثبت شد. سیگنال­های تحصیل شده توسط تبدیل سریع فوریه از حوزه­ی زمان به حوزه فرکانس انتقال یافتند. سپس 5 تابع ویژگی واریانس، انحراف از معیار، ریشه­ی میانگین مربعات، میانگین و کورتسیس از سیگنال‌های حوزه­ی زمان و فرکانس استخراج شدند. از دو طبقه­بند شبکه عصبی مصنوعی و ماشین بردار پشتیبان به منظور شناسایی سیگنال­های صدا استفاده شد. شبکه عصبی مصنوعی توانست به ترتیب با دقت 75 و 1/82 درصد و ماشین بردار پشتیبان نیز به ترتیب با دقت 7/85 و 8/92 درصد بر اساس سیگنال­های حوزه زمان و حوزه­ی فرکانس، سیگنال‌های صدای مربوط به اردک­های بیمار و سالم را از یکدیگر تشخیص دهند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Diagnosis of patients ducks based on their voices and using artificial intelligence methods

نویسندگان [English]

  • Ahmad Banakar 1
  • Meghdad Khazaei 2
1
2
چکیده [English]

In this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. For this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. Gained signals were transformed from time-domain signal to frequency domain using Fast Fourier Transform (FFT). Then, 5 statistical features are extracted from both time and frequency signals namely, mean, standard division, root mean square, variance and kurtosis. Two classifiers which are artificial neuralnetworks (ANN) and support vector machine (SVM) are used, in order to acquire the bird classification in healthy and sick accuracy. The accuracy of ANN classifier in detection of healthy birds within sick and weak birds was determined 75% and 82.1 % based on the time and frequency domain of the sound signals, respectively. The accuracy of SVM classifier in detection of healthy birds within sick and weak birds was determined 85.7 % and 92.8 % based on the time and frequency domain of the sound signals, respectively.

کلیدواژه‌ها [English]

  • sick birds’ detection
  • sound signals
  • Data Mining
  • artificial neural network (ANN)
  • Support Vector Machine (SVM)
Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., and Aide, T. M. (2009). Automated classification of bird and amphibian calls using machine learning: A comparison of methods. Ecological Informatics, 4(4), 206-214.
Al-Balushi, K. & Samanta, B. (2002). Gear fault diagnosis using energy-based features of acoustic emission signals. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 216(3), 249-263.
Banakar, A. & Azeem, M. F. (2008). Artificial wavelet neural network and its application in neuro-fuzzy models. Applied Soft Computing, 8(4), 1463-1485.
Banakar, A. & Karimi Akandi, S. (2012). a comparison of mathematical and artificial neural network modeling for rosa petals using hot air drying method. international journal of computational intelligence and applications, 11(02), 1-14.
Bardeli, R., Wolff, D., Kurth, F., Koch, M., Tauchert, K. H., & Frommolt, K. H. (2010). Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters, 31(12), 1524-1534.
Bracewell, R. N., & Bracewell, R. N. (1986). The Fourier transform and its applications (Vol. 31999). McGraw-Hill New York.
Catchpole, C. K. (1982). The Evolution of Bird Sounds in Relation to Mating and Spacing Behavior. Pages 297-319. Acoustic Communication in Birds. San Diego: Academic Press
Carr, C. E., & Soares, D. (2007). Shared Features of the Auditory System of Birds and Mammals. Evolution of Nervous Systems. H. K. Editor-in-Chief: Jon. Oxford, Academic Press: 443-457.
Chedad, A., Moshou, D., Aerts, J. M., Van Hirtum, A., Ramon, H., & Berckmans, D.. (2001). AP—Animal Production Technology: Recognition System for Pig Cough based on Probabilistic Neural Networks. Journal of agricultural engineering research, 79(4), 449-457.
Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Duhamel, P., & Vetterli, M. (1990). Fast Fourier transforms: a tutorial review and a state of the art. Signal Processing, 19(4), 259–299.
Engel, S. J., Gilmartin, B. J., Bongort, K., and Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining. Aerospace Conference Proceedings, 2000 IEEE, IEEE.
Exadaktylos, V., Silva, M., Aerts, J. M., Taylor, C. J., and Berckmans, D. (2008). Real-time recognition of sick pig cough sounds. Computers and electronics in agriculture, 63(2), 207-214.
Gasc, A., Sueur, J., Jiguet, F., Devictor, V., Grandcolas, P., Burrow, C., Depraetere, M., and Pavoine, S. (2013). Assessing biodiversity with sound: Do acoustic diversity indices reflect phylogenetic and functional diversities of bird communities?. Ecological Indicators, (25), 279-287.
Gaston, K. J., and O'Neill, M. A. (2004). Automated species identification: why not? Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, (359), 655-667.
Harma, A. (2003). Automatic identification of bird species based on sinusoidal modeling of syllables. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03), IEEE.
Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21(2), 688–705.
Ishibuchi, H., Nakashima, T., and Murata, T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(5), 601-618.
Jarvis, A. M. and Robertson, A. (1999). Predicting population sizes and priority conservation areas for 10 endemic Namibian bird species. Biological Conservation, 88(1), 121-131.
Kasten, E. P., McKinley, P. K. and Gage, S. H. (2010). Ensemble extraction for classification and detection of bird species. Ecological Informatics, 5(3), 153-166.
Khazaee, M., Ahmadi, H. Omid, M. & Khazaee, M. (2012). Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence. journal of vibroengineering, 14(2), 838-851.
Khazaee, M., Ahmadi, H. Omid, M. & Khazaee, M. (2014). Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster-Shafer evidence theory. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 228(1), 21-32.
King, A. P. & West, M. J. (1977). Species identification in the North American cowbird: appropriate responses to abnormal song. Science, 195(4282), 1002-1004.
Khazaee, M. (2012). Fault diagnosis & classification of their Characteristics for planetary gears using multi-sensory data fusion. MsC Thesis, University of Tehran. (In Farsi)
Lee, C.-H., Lee, Y.-K., and Huang, R. Z. (2006). Automatic recognition of bird songs using cepstral coefficients. Journal of Information Technology and Applications , 1(1), 17-23.
Lei, Y., He, Z., and Zi, Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 35, 1593-1600.
Liao, Y. & Vemuri, V. R. (2002). Use of K-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5), 439-448.
McKay, C., Fujinaga, I., Depalle, P. (2005). jAudio: A feature extraction library. Proceedings of the International Conference on Music Information Retrieval.
Miller, E. H. (1979). An approach to the analysis of graded vocalizations of birds. Behavioral and Neural Biology, 27(1), 25-38.
Nowak, M. A., Wagoner, R. V., Begelman, M. C., and Lehr, D. E. (1997). The 67 Hz Feature in the Black Hole Candidate GRS 1915+ 105 as a Possible. The Astrophysical Journal Letters, 477(2), 1-5.
Robertson, J., Harkin, C., and Govan, J. (1997) The Identification of Bird Feathers. Scheme for Feather Examination. Journal of the Forensic Science Society, 24: 85-98.
Salahshoor, K., Kordestani, M., and Khoshro, M. S. (2010). Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy, 35(12), 5472-5482.
Yang, J., Yang, J.-y., Zhang, D., & Lu, J. F.. (2003). Feature fusion: parallel strategy vs. serial strategy. Pattern Recognition, 36(6), 1369-1381.