Abstract
Music experience is closely associated with our moods and emotions. Even though data mining techniques have been widely adopted in computational analysis of music-emotion, traditional music including Sri Lankan folk music is less explored computationally. Therefore, considering a Sri Lankan folk music dataset, performed the classification using Support Vector Machines, Naive Bayes, Random Forest (RF), k-Nearest Neighbor (k-NN), and Logistic Regression (LR), employing dynamics, rhythm, timbre, pitch, and tonality features. k-NN achieved the maximum accuracy (78.44%) while RF and LR achieved accuracies of 76.19% and 73.42%, respectively. Combining the above three classifiers, an ensemble model was developed. Max-voting was applied, and the results were further enhanced using ensemble boosting. With optimized features, AdaBoost (RF as base estimator) achieved the highest accuracy (95.23%) while reducing the training time significantly. Expanding the dataset in terms of the number of music stimuli and emotion categories looked progressive.
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Charles, J., Lekamge, S. (2021). An Ensemble Learning Approach for Automatic Emotion Classification of Sri Lankan Folk Music. In: Shakya, S., Balas, V.E., Haoxiang, W., Baig, Z. (eds) Proceedings of International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol 176. Springer, Singapore. https://doi.org/10.1007/978-981-33-4355-9_23
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