Deep Learning–Based Emotion Classification Models for Chinese and Korean OST Music

Clicks: 68
ID: 312642
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #6 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Music Emotion Recognition (MER) has made significant advancements with deep learning, however, existing models tend to have cultural bias wherein they are not good at recognizing the emotion of non-Western musical structures. This paper proposes a deep learning framework designed especially for the emotion classification in Chinese and Korean Original Soundtracks (OSTs), which have unique tonal dynamics and a high variance in emotions. We propose a Dual-Stream Convolutional Recurrent Neural Network (CRNN) with Self-Attention, which is able to capture the spectral spatial characteristics and the temporal melodic developments, commonly found in Asian cinematic music. To validate the model, we use two region-specific datasets namely PMEmo (Chinese popular music) and EMOPIA (Korean/Asian piano OSTs). Experimental results show that our proposed architecture can obtain an accuracy of 88.4% and F1-score of 0.87, which outperforms baseline models (ResNet-50 and standard LSTM) with 5.2% margin. The research helps to confirm that the training data for culturally-aware training is vital for accurate affective computing within the music domain.
Reference Key
imported_1777055733_69ebb7f56db5f Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hyuntai Kim
Journal Journal of Computing & Biomedical Informatics
Year 2026
DOI
10.56979/1002/2026/1244
URL
https://jcbi.org/index.php/Main/article/view/1244
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.