Application of ABM to Spectral Features for Emotion Recognition

  • Semiye Demircan Department of Computer Engineering, Faculty of Engineering, Selcuk University/Konya Technical University, Konya, Turkey
  • Humar Kahramanli Department of Computer Engineering, Faculty of Engineering, Selcuk University/Konya Technical University, Konya, Turkey

Abstract

ER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features.

Published
Oct 1, 2018
How to Cite
DEMIRCAN, Semiye; KAHRAMANLI, Humar. Application of ABM to Spectral Features for Emotion Recognition. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 37, n. 4, p. 452-462, oct. 2018. ISSN 2413-7219. Available at: <http://publications.muet.edu.pk/index.php/muetrj/article/view/553>. Date accessed: 10 dec. 2018. doi: http://dx.doi.org/10.22581/muet1982.1804.01.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License