Abstract
The paper proposes classification of tabla strokes using multilayer feed forward artificial neural network. It uses 62 features extracted from the audio file as input units to the input layer and 13 tabla strokes as output units in the output layer. The classification has been done using dimension reduction and without using dimension reduction. The dimension reduction has been performed using Principal Component Analysis (PCA) which reduced the number of features from 62 to 28. The experiments have been performed on two sets of tabla strokes, which are played by professional tabla players, each comprises of 650 tabla strokes. The results demonstrate that correct classification of instances is more than 98 % in both the cases.
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Notes
- 1.
Tabla players were Mr. Arun Kundekar and Mr. Kshitij Patil, both of them are Sangeet Visharad in Tabla.
References
An Unquiet Mind: Western Classical Versus Indian Classical Music. http://skeptic.skepticgeek.com/2011/08/31/western-classical-vs-indian-classical-music/
David Courtney: Learning the Tabla, vol. 2. M. Bay Publications (2001). ISBN:0786607815
Chordia, P.: Segmentation and recognition of tabla strokes. In: International Conference on Music Information Retrieval, pp. 107–114. (2005)
Herrera, P., Dehamel, A., Gouyon, F.: Automatic labeling of unpitched percussion sounds. In: Audio Engineering Society Convention Paper. (2003)
Herrera, P., Yeterian, A., Gouyon, F.: Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques. In: International Conference on Music and Artificial Intelligence (ICMAI). (2002)
Kursa, M., Rudnicki, W., Wieczorkowska, A., Kubera, E., Kubik-Komar, A.: Musical instruments in random forest. Found. Intell. Syst. 5722, 281–290 (2009)
Kostek, B.: Automatic classification of musical instrument sounds. J. Acoust. Soc. Am. 107(5), 2818 (2000)
Cemgil, A.T., Gürgen, F.: Classification of musical instrument sounds using neural networks. Proc. SIU97 (1), 1–10 (1997)
Marques, J., Moreno, P.J.: A study of musical instrument classification using Gaussian mixture models and support vector machines. Cambridge Research Laboratory Technical Report Series, CRL 99/4(June). (1999)
Gillet, O., Richard, G.: Automatic labelling of tabla signals. In: ISMIR 2003, 4th International Conference on Music Information Retrieval, Baltimore, Maryland, USA, October 27–30. (2003)
Sarkar, M.: TablaNet : A Real-Time Online Musical Collaboration System for Indian Percussion. (2007)
Haykin, S.: Neural Networks. Macmillan, New York (1994)
Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Trans. Neural Netw. 1(4), 296–298 (1990)
Tzanetakis, G., Cook, P.: MARSYAS: a framework for audio analysis. Org. Sound 4(3), 169–175 (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Deolekar, S., Abraham, S. (2016). Classification of Tabla Strokes Using Neural Network. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_35
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DOI: https://doi.org/10.1007/978-81-322-2734-2_35
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