A Study on the Data Distribution Gap in Music Emotion Recognition

• URL: http://arxiv.org/abs/2510.04688v1
• Date: Mon, 06 Oct 2025 10:57:05 GMT
• Title: A Study on the Data Distribution Gap in Music Emotion Recognition
• Authors: Joann Ching, Gerhard Widmer,
• Abstract summary: Music Emotion Recognition (MER) is a task deeply connected to human perception.<n>Prior studies tend to focus on specific musical styles rather than incorporating a diverse range of genres.<n>We address the task of recognizing emotion from audio content by investigating five datasets with dimensional emotion annotations.
• Score: 7.281487567929003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music Emotion Recognition (MER) is a task deeply connected to human perception, relying heavily on subjective annotations collected from contributors. Prior studies tend to focus on specific musical styles rather than incorporating a diverse range of genres, such as rock and classical, within a single framework. In this paper, we address the task of recognizing emotion from audio content by investigating five datasets with dimensional emotion annotations -- EmoMusic, DEAM, PMEmo, WTC, and WCMED -- which span various musical styles. We demonstrate the problem of out-of-distribution generalization in a systematic experiment. By closely looking at multiple data and feature sets, we provide insight into genre-emotion relationships in existing data and examine potential genre dominance and dataset biases in certain feature representations. Based on these experiments, we arrive at a simple yet effective framework that combines embeddings extracted from the Jukebox model with chroma features and demonstrate how, alongside a combination of several diverse training sets, this permits us to train models with substantially improved cross-dataset generalization capabilities.

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