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amaai-lab/
music2emo

Name: music2emo
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Music2Emo: Towards Unified Music Emotion Recognition across Dimensional and Categorical Models

This repository contains the code accompanying the paper "Towards Unified Music Emotion Recognition across Dimensional and Categorical Models" by Dr. Jaeyong Kang and Prof. Dorien Herremans.

🔥 Live demo available on HuggingFace

Introduction

We present a unified multitask learning framework for Music Emotion Recognition (MER) that integrates categorical and dimensional emotion labels, enabling training across multiple datasets. Our approach combines musical features (key and chords) with MERT embeddings and employs knowledge distillation to enhance generalization. Evaluated on MTG-Jamendo, DEAM, PMEmo, and EmoMusic, our model outperforms state-of-the-art methods, including the best-performing model from the MediaEval 2021 competition.

Change Log

2025-02-10: Released Music2Emo v1.0, featuring both categorical and VA emotion prediction from music.

Quickstart Guide

Predict emotion from audio:

python

from music2emo import Music2emo

input_audio = "inference/input/test.mp3"

music2emo = Music2emo()
output_dic = music2emo.predict(input_audio)

valence = output_dic["valence"]
arousal = output_dic["arousal"]
predicted_moods =output_dic["predicted_moods"]

print("\n🎵 **Music Emotion Recognition Results** 🎵")
print("-" * 50)
print(f"🎭 **Predicted Mood Tags:** {', '.join(predicted_moods) if predicted_moods else 'None'}")
print(f"💖 **Valence:** {valence:.2f} (Scale: 1-9)")
print(f"⚡ **Arousal:** {arousal:.2f} (Scale: 1-9)")
print("-" * 50)

Installation

This repo is developed using python version 3.10

bash

git clone https://github.com/AMAAI-Lab/Music2Emotion
cd Music2Emotion
pip install -r requirements.txt

Our code is built on pytorch version 2.3.1 (torch==2.3.1 in the requirements.txt). But you might need to choose the correct version of torch based on your CUDA version

Dataset

Download the following datasets:

MTG-Jamendo (Link)
PMEmo (Link)
DEAM (Link)
EmoMusic (Link)

After downloading, place all .mp3 files into the following directory structure:

dataset/
├── jamendo/
│   └── mp3/**/*.mp3    # MTG-Jamendo audio files (nested structure)
├── pmemo/
│   └── mp3/*.mp3       # PMEmo audio files
├── deam/
│   └── mp3/*.mp3       # DEAM audio files
└── emomusic/
    └── mp3/*.mp3       # EmoMusic audio files

Directory Structure

config/: Configuration files
dataset/: Dataset directories
dataset_loader/: Dataset loading utilities
utils/: Other utilities
model/
- linear.py: Fully connected (FC) layer with MERT features
- linear_attn_ck.py: FC layer with MERT and musical features (chord/key)
- linear_mt_attn_ck.py: Multitask FC layer with MERT and musical features (chord/key)
preprocess/
- feature_extractor.py: MERT feature extraction
saved_models/: Saved model weight files
data_loader.py: Data loading script
train.py: Training script
test.py: Testing script
trainer.py: Training pipeline script
inference.py: Inference script
music2emo.py: Video2Music module that outputs emotion from input audio
demo.ipynb: Jupyter notebook for Quickstart Guide

Training

python train.py

Test

python test.py

Evaluation

Comparison of performance metrics when training on multiple datasets.

Training datasets	MTG-Jamendo (J.)	DEAM (D.)	EmoMusic (E.)	PMEmo (P.)
	PR-AUC / ROC-AUC	R² V / R² A	R² V / R² A	R² V / R² A
Single dataset (X)	0.1521 / 0.7806	0.5131 / 0.6025	0.5957 / 0.7489	0.5360 / 0.7772
J + D	0.1526 / 0.7806	0.5144 / 0.6046	-	-
J + E	0.1540 / 0.7809	-	0.6091 / 0.7525	-
J + P	0.1522 / 0.7806	-	-	0.5401 / 0.7780
J + D + E + P	0.1543 / 0.7810	0.5184 / 0.6228	0.6512 / 0.7616	0.5473 / 0.7940

Comparison of our proposed model with existing models on MTG-Jamendo dataset.

Model	PR-AUC ↑	ROC-AUC ↑
lileonardo	0.1508	0.7747
SELAB-HCMUS	0.1435	0.7599
Mirable	0.1356	0.7687
UIBK-DBIS	0.1087	0.7046
Hasumi et al.	0.0730	0.7750
Greer et al.	0.1082	0.7354
MERT-95M	0.1340	0.7640
MERT-330M	0.1400	0.7650
Proposed (Ours)	0.1543	0.7810

TODO

Incorporate additional features, such as lyrics.

Citation

If you find this resource useful, please cite the original work:

bibtex

@misc{kang2025unifiedmusicemotionrecognition,
      title={Towards Unified Music Emotion Recognition across Dimensional and Categorical Models}, 
      author={Jaeyong Kang and Dorien Herremans},
      year={2025},
      eprint={2502.03979},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.03979}, 
}

Kang, J. & Herremans, D. (2025). Towards Unified Music Emotion Recognition across Dimensional and Categorical Models, arXiv.

amaai-lab/music2emo