Dimensionality reduction and classification analysis on the audio section of the SEMAINE database
Document Type
Conference Proceeding
Publication Date
10-27-2011
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. © 2011 Springer-Verlag.
Publication Source (Journal or Book title)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
First Page
323
Last Page
331
Recommended Citation
Calix, R., Khazaeli, M., Javadpour, L., & Knapp, G. (2011). Dimensionality reduction and classification analysis on the audio section of the SEMAINE database. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6975 LNCS (PART 2), 323-331. https://doi.org/10.1007/978-3-642-24571-8_43
