Let's agree to disagree: Consensus Entropy Active Learning for Personalized Music Emotion Recognition

Previous research in music emotion recognition (MER) has tackled the inherent problem of subjectivity through the design of personalized models -- models which predict the emotions that a particular user would perceive from music. Personalized models are trained in a supervised manner, and are tested exclusively with the annotations provided by a specific user. While past research has focused on model adaptation or reducing the amount of annotations required from a given user, we propose a novel methodology based on uncertainty sampling and query-by-committee methods, adopting prior knowledge from the agreement of human annotations as an oracle for active learning. We assume that our disagreements define our personal opinions and should be considered for personalization. We use the DEAM dataset, the current benchmark dataset for MER, to pre-train our models. We then use the AMG1608 dataset, the largest MER dataset containing multiple annotations per musical excerpt, to re-train diverse machine learning models using active learning and evaluate personalization. Our results suggest that our methodology can be beneficial to produce personalized classification algorithms, which exhibit different results depending on the algorithms' complexity.