A New Multilabel System for Automatic Music Emotion Recognition
Abstract
Achieving advancements in automatic recognition of emotions that music can induce require considering multiplicity and simultaneity of emotions. Comparison of different machine learning algorithms performing multilabel and multiclass classification is the core of our work. The study analyzes the implementation of the Geneva Emotional Music Scale 9 in the Emotify music dataset and investigates its adoption from a machine-learning perspective. We approach the scenario of emotions expression/induction through music as a multilabel and multiclass problem, where multiple emotion labels can be adopted for the same music track by each annotator (multilabel), and each emotion can be identified or not in the music (multiclass). The aim is the automatic recognition of induced emotions through music.
Cite
@article{arxiv.1905.12629,
title = {A New Multilabel System for Automatic Music Emotion Recognition},
author = {Fabio Paolizzo and Natalia Pichierri and Daniele Casali and Daniele Giardino and Marco Matta and Giovanni Costantini},
journal= {arXiv preprint arXiv:1905.12629},
year = {2021}
}
Comments
2 tables. Research supported by the EU through the MUSICAL-MOODS project funded by the Marie Sklodowska-Curie Actions Individual Fellowships Global Fellowships (MSCA-IF-GF) of the Horizon 2020 Programme H2020/2014-2020, REA grant agreement n.659434