DocumentCode :
1799670
Title :
CCNF for continuous emotion tracking in music: Comparison with CCRF and relative feature representation
Author :
Imbrasaite, Vaiva ; Baltrusaitis, Tadas ; Robinson, Peter
Author_Institution :
Comput. Lab., Univ. of Cambridge, Cambridge, UK
fYear :
2014
fDate :
14-18 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
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.
Keywords :
emotion recognition; learning (artificial intelligence); music; regression analysis; support vector machines; CCNF; CCRF; continuous conditional neural fields model; continuous emotion tracking; machine learning; model-level feature fusion; music; relative feature representation; similar continuous conditional random fields model; support vector regression; Correlation; Feature extraction; Kernel; Measurement; Standards; Training; Vectors; Music emotion recognition; continuous tracking; dimensional representation; machine learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on
Conference_Location :
Chengdu
ISSN :
1945-7871
Type :
conf
DOI :
10.1109/ICMEW.2014.6890697
Filename :
6890697
Link To Document :
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