Knowledge structuring for database mining and text retrieval using past optimal queries
Knowledge structuring for database mining and text retrieval using past optimal queries
FLAME—Fuzzy Logic Adaptive Model of Emotions
Autonomous Agents and Multi-Agent Systems
Music emotion recognition: the role of individuality
Proceedings of the international workshop on Human-centered multimedia
In the Mood: Tagging Music with Affects
Affect and Emotion in Human-Computer Interaction
Building emotion lexicon from weblog corpora
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
A haptic emotional model for audio system interface
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Exploiting online music tags for music emotion classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Affective acoustic ecology: towards emotionally enhanced sound events
Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound
Personalized music emotion classification via active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Human-centric music medical therapy exploration system
Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
International Journal of Online Pedagogy and Course Design
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Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.