Dimensionality reduction and classification analysis on the audio section of the SEMAINE database

Authors:
Ricardo A. Calix;Mehdi A. Khazaeli;Leili Javadpour;Gerald M. Knapp
Affiliations:
Louisiana State University, Industrial Engineering, Patrick F. Taylor Hall, Baton Rouge, LA;Louisiana State University, Industrial Engineering, Patrick F. Taylor Hall, Baton Rouge, LA;Louisiana State University, Industrial Engineering, Patrick F. Taylor Hall, Baton Rouge, LA;Louisiana State University, Industrial Engineering, Patrick F. Taylor Hall, Baton Rouge, LA
Venue:
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Year:
2011

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Abstract

This paper presents an analysis of the audio section of the SEMAINE database for affect detection. Chi-square and principal component analysis techniques are used to reduce the dimensionality of the audio datasets. After dimensionality reduction, different classification techniques are used to perform emotion classification at the word level. Additionally, for unbalanced training sets, class re-sampling is performed to improve the model's classification results. Overall, the final results indicate that Support Vector Machines (SVM) performed best for all data sets. Results show promise for the SEMAINE database as an interesting corpus to study affect detection.