TAFFC 2025 Music Emotion: Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges

Music has long been known to evoke powerful emotions, but can machines truly understand and predict these emotional responses? This survey paper takes stock of the field of music emotion recognition (MER), examining the datasets, computational models, and persistent challenges that shape this research area. The authors review how emotion is represented—from categorical labels to dimensional models like valence-arousal—and analyze the most widely used datasets including the Million Song Dataset and MediaEval benchmarks. They trace the evolution from traditional machine learning approaches using hand-crafted audio features to modern deep learning architectures. Despite significant progress, the paper identifies fundamental challenges: the subjective nature of emotional responses to music, the difficulty of obtaining reliable ground truth labels, and the gap between controlled laboratory studies and real-world listening contexts. Jaeyong Kang and Dorien Herremans. 2024. Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges. IEEE Transactions on Affective Computing, vol. 16, no. 4, 2024. https://doi.org/10.1109/TAFFC.2025.3583505