Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering

• Published in: 2010 International Conference on Machine Learning and Applications pp. 655 - 660
• Main Authors: Schmidt, E M, Kim, Y E
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.12.2010
• Subjects: Acoustics; audio features; Emotion recognition; Games; Kalman filtering; Kalman filters; linear dynamical systems; Mood; Noise; Predictive models; regression; Testing
• ISBN: 1424492114; 9781424492114
• DOI: 10.1109/ICMLA.2010.101

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 time-varying, 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 regression. 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 "genie-bounded" 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.