Machine Learning Algorithms for Indian Music Classification Based on Raga Framework
Kalyani C. Waghmare1, Balwant A. Sonkamble2
1Kalyani C. Waghmare*, Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.
2Balwant A. Sonkamble, Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.
Manuscript received on August 20, 2020. | Revised Manuscript received on September 02, 2020. | Manuscript published on September 10, 2020. | PP: 130-134 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K77240991120 | DOI: 10.35940/ijitee.K7724.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The supervised and unsupervised learning methods in Machine Learning are successfully applied to solve various real time problems in different domains. The Indian Music has a base of Raga structure. The Raga is melodious framework for composition and improvisation. The identification and indexing of Raga for Indian Music data will improve efficiency and accuracy of retrieval being expected by e-learners, composers and classical music listeners. The identification of Raga in Indian Music is very difficult task for naïve user. The application of machine learning algorithms will definitely be best key idea. The paper demonstrates K-means and Agglomerative clustering methods from unsupervised learning nonetheless K Nearest Neighbor, Decision Tree and Support Vector Machine and Naïve Bayes classifiers are implemented from supervised learning. The partition of 70:30 is done for training data and testing data. Pitch Class Distribution features are extracted by identifying Pitch for every frame in an audio signal using Autocorrelation method. The comparison of above algorithms is done and observed supervised learning methods outperformed.
Keywords: Classification, Clustering, Indian Classical Raga, Performance measures, Pitch Features.
Scope of the Article: Machine Learning