Conference proceeding
Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering
2010 Ninth International Conference on Machine Learning and Applications, pp 655-660
Dec 2010
Abstract
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.
Metrics
4 Record Views
42 citations in Scopus
Details
- Title
- Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering
- Creators
- E M Schmidt - Drexel UniversityY E Kim - Drexel University
- Publication Details
- 2010 Ninth International Conference on Machine Learning and Applications, pp 655-660
- Conference
- 2010 9th International Conference on Machine Learning and Applications, 9th
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-79952403274
- Other Identifier
- 991019173996304721