# A Study on the Data Distribution Gap in Music Emotion Recognition

arXiv: https://arxiv.org/abs/2510.04688 · HTML: https://mlsanity.com/paper/2510.04688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [A Foundation Model for Music Informatics](https://mlsanity.com/paper/2311.03318/) (2023)
- [Music2Latent: Consistency Autoencoders for Latent Audio Compression](https://mlsanity.com/paper/2408.06500/) (2024)
- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition](https://mlsanity.com/paper/2104.06517/) (2021)
- [Towards Explainable Music Emotion Recognition: The Route via Mid-level Features](https://mlsanity.com/paper/1907.03572/) (2019)
- [Regression-based music emotion prediction using triplet neural networks](https://mlsanity.com/paper/2001.09988/) (2020)
- [Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges](https://mlsanity.com/paper/2406.08809/) (2024)
- [On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features](https://mlsanity.com/paper/2107.13231/) (2021)
- [A Comparative Study of Western and Chinese Classical Music based on Soundscape Models](https://mlsanity.com/paper/2002.09021/) (2020)

## Abstract (arXiv)

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.