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 (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 fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.
We also note that most existing work focuses on the aspect of music emotion research, namely MER. Little work has been made to the aspect -- the development of emotion-based music retrieval systems @cite_76 . In what follows, we present the AEG model and its applications to the both of these two aspects.
@cite_76
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One of the most exciting but challenging endeavors in music research is to develop a computational model that comprehends the affective content of music signals and organizes a music collection according to emotion. In this paper, we propose a novel acoustic emotion Gaussians (AEG) model that defines a proper generative process of emotion perception in music. As a generative model, AEG permits easy and straightforward interpretations of the model learning processes. To bridge the acoustic feature space and music emotion space, a set of latent feature classes, which are learned from data, is introduced to perform the end-to-end semantic mappings between the two spaces. Based on the space of latent feature classes, the AEG model is applicable to both automatic music emotion annotation and emotion-based music retrieval. To gain insights into the AEG model, we also provide illustrations of the model learning process. A comprehensive performance study is conducted to demonstrate the superior accuracy of AEG over its predecessors, using two emotion annotated music corpora MER60 and MTurk. Our results show that the AEG model outperforms the state-of-the-art methods in automatic music emotion annotation. Moreover, for the first time a quantitative evaluation of emotion-based music retrieval is reported.