Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Personalized music emotion classification via active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Exploring the relationship between categorical and dimensional emotion semantics of music
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
The acoustic emotion gaussians model for emotion-based music annotation and retrieval
Proceedings of the 20th ACM international conference on Multimedia
Human-centric music medical therapy exploration system
Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
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Typically, a machine learning model of automatic music emotion recognition is trained to learn the relationship between music features and perceived emotion values. However, simply assigning an emotion value to a clip in the training phase does not work well because the perceived emotion of a clip varies from person to person. To resolve this problem, we propose a novel approach that represents the perceived emotion of a clip as a probability distribution in the emotion plane. In addition, we develop a methodology that predicts the emotion distribution of a clip by estimating the emotion mass at discrete samples of the emotion plane. We also develop model fusion algorithms to integrate different perceptual dimensions of music listening and to enhance the modeling of emotion perception. The effectiveness of the proposed approach is validated through an extensive performance study. An average R2 statistics of 0.5439 for emotion prediction is achieved. We also show how this approach can be applied to enhance our understanding of music emotion.