Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering

Erik M. Schmidt, Youngmoo E. Kim
2010 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-variate
more » ... sion. In this paper, we extend our framework to account for multiple regression mappings contingent upon a general location in A-V space. Furthermore, we model A-V state as the latent variable of a linear dynamical system, more explicitly capturing the dynamics of musical mood. We validate this extension using a "geniebounded" approach, in which we assume that a piece of music is correctly clustered in A-V space a priori, demonstrating significantly higher theoretical performance than the previous single-regressor approach.
doi:10.1109/icmla.2010.101 dblp:conf/icmla/SchmidtK10 fatcat:zzghqb45oneblcgrfot2phokcm