MERGE -- A Bimodal Dataset for Static Music Emotion Recognition
<|reference_start|>MERGE -- A Bimodal Dataset for Static Music Emotion Recognition: 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.<|reference_end|>
@article{louro2024merge,
    title={MERGE -- A Bimodal Dataset for Static Music Emotion Recognition},
    author={Pedro Lima Louro, Hugo Redinho, Ricardo Santos, Ricardo Malheiro,
  Renato Panda, Rui Pedro Paiva},
    journal={arXiv preprint arXiv:2407.06060},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06060},
    primaryClass={cs.SD cs.IR cs.LG cs.MM eess.AS}
}