CCNF for continuous emotion tracking in music: Comparison with CCRF and relative feature representation

Whether or not emotion in music can change over time is not a question that requires discussion. As the interest in continuous emotion prediction grows, there is a greater need for tools that are suitable for dimensional emotion tracking. In this paper, we propose a novel Continuous Conditional Neural Fields model that is designed specifically for such a problem. We compare our approach with a similar Continuous Conditional Random Fields model and Support Vector Regression showing a great improvement over the baseline. Our new model is especially well suited for hierarchical models such as model-level feature fusion, which we explore in this paper. We also investigate how well it performs with relative feature representation in addition to the standard representation.