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

TL;DR

This survey reviews music emotion prediction datasets, models, and challenges, emphasizing the need for standardized benchmarks, larger datasets, and better model interpretability to advance the field.

Contribution

It provides a comprehensive overview of existing datasets, models, and challenges in music emotion prediction, highlighting key issues and future directions.

Findings

Persistent challenges include dataset quality and annotation ambiguity.

Cross-dataset generalization remains difficult.

Standardized benchmarks and larger datasets are needed.

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. Through this examination, we identify persistent challenges in music emotion recognition (MER), including issues related to dataset quality, the ambiguity in emotion labels, and the difficulties of cross-dataset generalization. We argue that future advancements in MER require standardized benchmarks, larger and more diverse datasets, and improved model interpretability. 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.