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Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering
2010 Ninth International Conference on Machine Learning and Applications
The medium of music has evolved specifically for the expression of emotions, and it is natural for us to organize music in terms of its emotional associations. In previous work, we have modeled human response labels to music in the arousal-valence (A-V) representation of affect as a timevarying, stochastic distribution reflecting the ambiguous nature of the perception of mood. These distributions are used to predict A-V responses from acoustic features of the music alone via multi-variatedoi:10.1109/icmla.2010.101 dblp:conf/icmla/SchmidtK10 fatcat:zzghqb45oneblcgrfot2phokcm