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http://dx.doi.org/10.7472/jksii.2022.23.2.45

Opera Clustering: K-means on librettos datasets  

Jeong, Harim (Dept. of Interaction Science, Sungkyunkwan University)
Yoo, Joo Hun (Dept. of Artificial Intelligence, Sungkyunkwan University)
Publication Information
Journal of Internet Computing and Services / v.23, no.2, 2022 , pp. 45-52 More about this Journal
Abstract
With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.
Keywords
Music Analysis; Music Information Retrieval; Natural Language Processing; Embedding; Classification; K-means Clustering;
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1 Wang, Xinxi, et al. "Exploration in interactive personalized music recommendation: a reinforcement learning approach." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol.11, No.1, pp.1-22, 2014. https://doi.org/10.1145/2623372   DOI
2 Bahuleyan, Hareesh. "Music genre classification using machine learning techniques." arXiv preprint arXiv:1804.01149, 2018. https://arxiv.org/abs/1804.01149
3 Costa, Yandre MG, Luiz S. Oliveira, and Carlos N. Silla Jr. "An evaluation of convolutional neural networks for music classification using spectrograms." Applied soft computing, vol.52, pp.28-38, 2017. https://doi.org/10.1016/j.asoc.2016.12.024   DOI
4 Weiss, Christof, and Meinard Muller. "Tonal complexity features for style classification of classical music." 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. https://doi.org/10.1109/ICASSP.2015.7178057   DOI
5 Jiang, D. N., Lu, L., Zhang, H. J., Tao, J. H., and Cai, L. H. "Music type classification by spectral contrast feature." IEEE International Conference on Multimedia and Expo, pp.113-116, 2002. https://doi.org/10.1109/ICME.2002.1035731   DOI
6 MacMurray, Jessica M., and Allison B. F. "The Book of 101 Opera Librettos: Complete Original Language Texts with English Translations," New York: Black Dog & Leventhal Publishers, 1996.
7 Ye, Meng, and Yuhong G. "Zero-Shot Classification with Discriminative Semantic Representation Learning." CVF Open Access, January 1, 1970. https://doi.org/10.1109/cvpr.2017.542   DOI
8 Jack, Rachael E., Wei S, Ioannis D, Oliver G. Garrod, and Philippe G. Schyns. "Four Not Six: Revealing Culturally Common Facial Expressions of Emotion." Journal of Experimental Psychology: General, 145(6), pp.708-730, 2016. https://doi.org/10.1037/xge0000162   DOI
9 Le, Quoc, and Tomas M. "Distributed Representations of Sentences and Documents." PMLR., June 18, 2014. http://proceedings.mlr.press/v32/le14.html
10 Meyers O.C. "A Mood-Based Music Classification and Exploration System," Massachusetts Institue of Technology, 2007. https://dspace.mit.edu/handle/1721.1/39337
11 Silla Jr., Carlos N., Alessandro L. Koerich, and Celso A. Kaestner. "A Machine Learning Approach to Automatic Music Genre Classification," Journal of the Brazilian Computer Society, vol.14, no.3, pp.7-18, 2008. https://doi.org/10.1590/s0104-65002008000300002   DOI
12 Milligan "Clustering and Classification Methods." Handbook of Psychology, 2003. https://doi.org/10.1002/0471264385.wei0207.   DOI
13 Van Den Oord, Aaron, Sander Dieleman, and Benjamin Schrauwen. "Deep content-based music recommendation." Neural Information Processing Systems Conference (NIPS 2013). Vol. 26. Neural Information Processing Systems Foundation (NIPS), 2013.
14 H Jeong, "Study on sentiment analysis for Opera." in Proc. of APIC-IST 2021, 89-91, 2021.
15 Haggblade, Michael, Yang Hong, and Kenny Kao. "Music genre classification." Department of Computer Science, Stanford University, 2011.
16 Nanni, Loris, et al. "Combining visual and acoustic features for music genre classification." Expert Systems with Applications, Vol.45, pp.108-117, 2016. https://doi.org/10.1016/j.eswa.2015.09.018   DOI