The Music Emotion Recognition (MER) field has seen steady developments in recent years, with contributions from feature engineering, machine learning, and deep learning. The landscape has also shifted from audio-centric systems to bimodal ensembles that combine audio and lyrics. However, a severe lack of public and sizeable bimodal databases has hampered the development and improvement of bimodal audio-lyrics systems. This article proposes three new audio, lyrics, and bimodal MER research datasets, collectively called MERGE, created using a semi-automatic approach. To comprehensively assess the proposed datasets and establish a baseline for benchmarking, we conducted several experiments for each modality, using feature engineering, machine learning, and deep learning methodologies. In addition, we propose and validate fixed train-validate-test splits. The obtained results confirm the viability of the proposed datasets, achieving the best overall result of 79.21% F1-score for bimodal classification using a deep neural network.
View on arXiv@article{louro2025_2407.06060,
title={ MERGE -- A Bimodal Dataset for Static Music Emotion Recognition },
author={ Pedro Lima Louro and Hugo Redinho and Ricardo Santos and Ricardo Malheiro and Renato Panda and Rui Pedro Paiva },
journal={arXiv preprint arXiv:2407.06060},
year={ 2025 }
}