Multilabel Automated Recognition of Emotions Induced Through Music

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 the data distribution. The research goal is the identification of best methods towards the definition of the audio component of a new a new multimodal dataset for music emotion recognition.