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

• URL: http://arxiv.org/abs/2406.08809v2
• Date: Tue, 22 Oct 2024 12:18:27 GMT
• Title: Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges
• Authors: Jaeyong Kang, Dorien Herremans,
• Abstract summary: We provide a comprehensive overview of the available music-emotion datasets and discuss evaluation standards as well as competitions in the field. We highlight the challenges that persist in accurately capturing emotion in music, including issues related to dataset quality, annotation consistency, and model generalization. We have complemented our findings with an accompanying GitHub repository.
• Score: 9.62904012066486
• License:
• Abstract: Deep learning models for music have advanced drastically in recent years, but how good are machine learning models at capturing emotion, 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 offer a brief overview of various types of music emotion prediction models that have been built over the years, providing insights into the diverse approaches within the field. Through this examination, we highlight the challenges that persist in accurately capturing emotion in music, including issues related to dataset quality, annotation consistency, and model generalization. Additionally, we explore the impact of different modalities, such as audio, MIDI, and physiological signals, on the effectiveness of emotion prediction models. 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.

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