Linear regression-based adaptation of music emotion recognition models for personalization
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Pages
2149-2153
Date Issued
2014
Author(s)
Abstract
Personalization techniques can be applied to address the subjectivity issue of music emotion recognition, which is important for music information retrieval. However, achieving satisfactory accuracy in personalized music emotion recognition for a user is difficult because it requires an impractically huge amount of annotations from the user. In this paper, we adopt a probabilistic framework for valence-arousal music emotion modeling and propose an adaptation method based on linear regression to personalize a background model in an online learning fashion. We also incorporate a component-tying strategy to enhance the model flexibility. Comprehensive experiments are conducted to test the performance of the proposed method on three datasets, including a new one created specifically in this work for personalized music emotion recognition. Our results demonstrate the effectiveness of the proposed method. © 2014 IEEE.
SDGs
Type
conference paper