한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
Opera Clustering: K-means on librettos datasets |
| 영문제목(English Title) |
Opera Clustering: K-means on librettos datasets |
| 저자(Author) |
정하림
유주헌
Harim Jeong
Joohun Yoo
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| 원문수록처(Citation) |
VOL 23 NO. 02 PP. 0045 ~ 0052 (2022. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
Music Analysis
music information retrieval
Natural Language Processing
Embedding
Classification
K-Means Clustering
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| 파일첨부 |
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