Affective Music Information Retrieval
Ju-Chiang Wang, Yi-Hsuan Yang, Hsin-Min Wang

TL;DR
This paper introduces a novel probabilistic generative model called Acoustic Emotion Gaussians (AEG) for modeling music emotions in the valence-arousal space, improving emotion recognition and retrieval in music information retrieval.
Contribution
The paper presents a new Gaussian mixture model approach that captures emotion perception subjectivity and supports online learning for personalized and general emotion-based music applications.
Findings
AEG effectively models emotion perception in music.
The model performs well on large-scale emotion-annotated datasets.
AEG supports both user-independent and user-dependent emotion recognition.
Abstract
Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing
