Using artificial intelligence to analyze and classify music emotion

Article type: Research Article

Authors: Liu, Hongyu

Affiliations: Henan Information and Statistics Vocational College, Zhengzhou, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: Henan Information and Statistics Vocational College, Zhengzhou, China. E-mail: [email protected].

Abstract: With the rapid development of music digitization and online streaming services, automatic analysis and classification of music content has become an urgent need. This research focuses on music sentiment analysis, which is the identification and classification of emotions expressed by music through algorithms. The study defines and classifies possible emotions in music. Then, advanced artificial intelligence techniques, including traditional machine learning and deep learning methods, were employed to perform sentiment analysis on music fragments. In the process of creating and validating the model, the combination of convolutional neural network and long term memory network shows excellent performance in various performance indicators. However, for some complex or culturally ambiguous music fragments, the model may also suffer from misclassification problems. This provides the direction for further optimization of future research aimed at achieving more accurate music emotion analysis.

Keywords: Music emotion analysis, artificial intelligence, deep learning, music classification, cultural difference

DOI: 10.3233/JCM-247488

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2611-2628, 2024