@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}
}
@conference {2025,
	title = {Towards Unified Music Emotion Recognition across Dimensional and Categorical Models},
	year = {2025},
	url = {https://arxiv.org/abs/2502.03979},
	author = {J. Kang and D. Herremans}
}
@conference {2023,
	title = {A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling},
	booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
	year = {2023},
	publisher = {AAAI},
	organization = {AAAI},
	address = {Washington DC},
	abstract = {Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modeling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modeling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations. The code of the proposed method is available online. },
	author = {Zixun Guo and J. Kang and D. Herremans}
}
