MERGE -- A Bimodal Dataset for Static Music Emotion Recognition

Autor:	Louro, Pedro Lima, Redinho, Hugo, Santos, Ricardo, Malheiro, Ricardo, Panda, Renato, Paiva, Rui Pedro
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Information Retrieval Computer Science - Machine Learning Computer Science - Multimedia Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 4 figures, 13 tables, submitted to IEEE Transactions on Affective Computing
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.06060 Zobrazit plný text záznamu View this record from Arxiv