Current Science

Open Access Open Access  Restricted Access Subscription Access

Tree-Based Classification of Tabla Strokes


Affiliations
1 Department of Computer Science, University of Mumbai, Mumbai 400 098, India
2 Department of Maths and Stats, G. N. Khalsa College, University of Mumbai, Mumbai 400 019, India
 

This study attempts to validate the effectiveness of tree classifiers to classify tabla strokes especially the ones which overlap in nature. It uses decision tree, ID3 and random forest as classifiers. A custom made data set of 650 samples of 13 different tabla strokes were used for experimental purpose. Thirty-one different features with their mean and variances were extracted for classification. Three data sets consisting of 21,361, 18,802 and 19,543 instances respectively, were used for the purpose. Validation was done using measures like receiver operating characteristic curve and accuracy. All the classifiers showed excellent results with random forest outperforming the other two. The effectiveness of random forest in classifying strokes which overlap in nature is evaluated by comparing the known results with multi-layer perceptron.

Keywords

Classification, Decision Tree, Random Forest, Tree Classifiers, Tabla Strokes.
User
Notifications
Font Size

  • Sagar, S. N., A comparative study of classification techniques in data mining algorithms. Orient. J. Comput. Sci. Technol., 2015, 8(1), 13–19.

  • Yan-yan, S. and Ying, L. Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry, 2015, 27(2), 130–135.

  • Moon, S. S., Kang, S.-Y., Jitpitaklert, W. and Kim, S. B., Decision tree models for characterizing smoking patterns of older adults. Expert Syst. Appl., 2012, 39(1), 445–451.

  • Wilton, W. T., Fok, H. C., Yi, J., Li, S., Au Yeung, H. H., Ying, W. and Fang, L., Data mining application of decision trees for student profiling at the Open University of China. In IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, Beijing, 2014, pp. 732–738.

  • Lu, J., Liu, Y. and Li, X., The decision tree application in agricultural development. In Artificial Intelligence and Computational Intelligence (eds Deng, H. et al.), AICI 2011, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 7002.

  • Franco-Arcega, A., Flores-Flores, L. G. and Gabbasov, R. F., Application of decision trees for classifying astronomical objects. In 12th Mexican International Conference on Artificial Intelligence, Mexico City, 2013, pp. 181–186.

  • Yu, G. and Wenjuan, G., Decision tree method in financial analysis of listed logistics companies. In International Conference on Intelligent Computation Technology and Automation, Changsha, 2010, pp. 1101–1106.

  • Srinivasan Ramaswamy, Multiclass text classification a decision tree based SVM Approach, CS294 Practical Machine Learning Project, Citeseer, 2006.

  • Kursa, M., Rudnicki, W., Wieczorkowska, A., Kubera, E. and Kubik-Komar, A., Musical instruments in random forest. In Foundations of Intelligent Systems (eds Rauch, J. et al.), ISMIS 2009. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 5722.

  • Lavner, Y. and Ruinskiy, D., A decision-tree-based algorithm for speech/music classification and segmentation. EURASIP J. Audio, Speech, Music Processing, 2009.

  • Deolekar, S. and Abraham, S., Classification of tabla strokes using neural network. In Computational Intelligence in Data Mining – Volume 1 (eds Behera, H. and Mohapatra, D.), Advances in Intelligent Systems and Computing, Springer, New Delhi, 2016, vol. 410.

  • Courtney, D., Learning the Tabla, M. Bay Publications, 2001, vol. 2, ISBN 0786607815.

  • Chordia, P., Segmentation and recognition of tabla strokes. International Conference on Music Information Retrieval, 2005, pp. 107–114.

  • Navada, A., Ansari, A. N., Patil, S. and Sonkamble, B. A., Overview of use of decision tree algorithms in machine learning. In IEEE Control and System Graduate Research Colloquium (ed. Shah Alam), 2011, pp. 37–42.

  • Jin, C., De-lin, L. and Fen-Xiang, M., An improved ID3 decision tree algorithm. In the Proceedings of 4th International Conference on Computer Science and Education, 2009, pp. 127–130.

  • Qi, Y., Random forest for bioinformatics. In Ensemble Machine Learning (eds Zhang, C. and Ma, Y.), Springer, Boston, MA, 2012, pp. 307–323.

  • Dubrava, S. et al., Using random forest models to identify correlates of a diabetic peripheral neuropathy diagnosis from electronic health record data. Pain Med., 2017, 34, 107–115.

  • Saha, D., Alluri, P. and Gan, A., A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection. J. Adv. Transport., 2016, 50, 522–540.

  • Armando Vieira, Predicting online user behaviour using deep learning algorithms. Computing Research Repository – arXiv.org, 2015, http://arxiv.org/abs/1511.06247.

  • Brent, W., Physical and perceptual aspects of percussive timbre, UCSanDiego Electronic Theses and Dissertations, 2010.

  • Chattamvelli, R., Data Mining Methods, Alpha Science International, Oxford, UK, 2009.

  • Audacity® software is copyright © 1999–2017 Audacity Team. website: https://audacityteam.org/. It is free software distributed under the terms of the GNU General Public License. The name Audacity® is a registered trademark of Dominic Mazzoni.

  • George Tzanetakis and Perry Cook, MARSYAS: a framework for audio analysis. Org. Sound, 1999, 4(3), 169–175.


Abstract Views: 349

PDF Views: 116




  • Tree-Based Classification of Tabla Strokes

Abstract Views: 349  |  PDF Views: 116

Authors

Subodh Deolekar
Department of Computer Science, University of Mumbai, Mumbai 400 098, India
Siby Abraham
Department of Maths and Stats, G. N. Khalsa College, University of Mumbai, Mumbai 400 019, India

Abstract


This study attempts to validate the effectiveness of tree classifiers to classify tabla strokes especially the ones which overlap in nature. It uses decision tree, ID3 and random forest as classifiers. A custom made data set of 650 samples of 13 different tabla strokes were used for experimental purpose. Thirty-one different features with their mean and variances were extracted for classification. Three data sets consisting of 21,361, 18,802 and 19,543 instances respectively, were used for the purpose. Validation was done using measures like receiver operating characteristic curve and accuracy. All the classifiers showed excellent results with random forest outperforming the other two. The effectiveness of random forest in classifying strokes which overlap in nature is evaluated by comparing the known results with multi-layer perceptron.

Keywords


Classification, Decision Tree, Random Forest, Tree Classifiers, Tabla Strokes.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi9%2F1724-1731