Abstract
The paper presents research on the problem of pattern recognition applied to the analysis of basic units of ancient Russian chants. We take for testing two types of neural networks: Multilayer Perceptron (MLP) and Probabilistic Neural Network (PNN). We investigate main features of the chant units and the properties of the networks to choose the best structure and algorithm. The results provide an analysis of accuracy for both approaches used in solving this particular task.
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References
Golubeva, I.V., Philippovich, A.Yu.: Syntactic analysis musical texts. Novye informatsionnye tekhnologii v avtomatizirovannykh sistemakh. 16, 257–262 (2013) - in Russian
Philippovich, A.Yu., Danshina, M.V.: Methods for automatization of the process of decoding of znamenny chants. In: International Conference in Cognitive Linguistics, CrossLingua’2013: Cognition. Communication. Culture, Ukraine, Crimea (2013) - in Russian
Zelentsov, I.A., Philippovich, Yu.N.: Recognition of letters and words in ancient Russian cursive writing. Sci. Educ. 12, 27 (2011) - in Russian
Torgerson, W.S.: Theory and Methods of Scaling. Wiley, New York (1958). ISBN 0-89874-722-8
Specht, D.F.: Probabilistic neural networks. Neural Netw. 3, 109118 (1990)
Acknowledgements
The research is supported by grant 11-04-12025 of Russian Humanitarian Scientific Fund.
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© 2014 Springer International Publishing Switzerland
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Vylomova, E., Philippovich, A., Danshina, M., Golubeva, I., Philippovich, Y. (2014). Neural Models for Recognition of Basic Units of Semiographic Chants. In: Ignatov, D., Khachay, M., Panchenko, A., Konstantinova, N., Yavorsky, R. (eds) Analysis of Images, Social Networks and Texts. AIST 2014. Communications in Computer and Information Science, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-319-12580-0_26
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DOI: https://doi.org/10.1007/978-3-319-12580-0_26
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