@article {2024,
	title = {Are we there yet? A brief survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges},
	journal = {IEEE Transactions on Affective Computing},
	year = {2025},
	abstract = {Deep learning models for music have advanced drastically in the last few years. But how good are machine learning models at capturing emotion these days and what challenges are researchers facing? In this paper, we provide a comprehensive overview of the available music-emotion datasets and discuss evaluation standards as well as competitions in the field. We also provide a brief overview of various types of music emotion prediction models that have been built over the years, offering insights into the diverse approaches within the field. Through this examination, we highlight the challenges that persist in accurately capturing emotion in music. Recognizing the dynamic nature of this field, we have complemented our findings with an accompanying GitHub repository. This repository contains a comprehensive list of music emotion datasets and recent predictive models.},
	url = {https://arxiv.org/abs/2406.08809},
	author = {J. Kang and D. Herremans}
}
