Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method

2012 ◽  
Vol 23 (1) ◽  
pp. 215-227 ◽  
Author(s):  
Mansour Sheikhan ◽  
Mahdi Bejani ◽  
Davood Gharavian
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Feature Selection Method ◽  
Selection Method ◽  
Speech Emotion Recognition

scholarly journals A Hybrid Meta-Heuristic Feature Selection Method Using Golden Ratio and Equilibrium Optimization Algorithms for Speech Emotion Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 200953-200970
Author(s):  
Arijit Dey ◽  
Soham Chattopadhyay ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Golden Ratio ◽  
Optimization Algorithms ◽  
Feature Selection Method ◽  
Selection Method ◽  
Speech Emotion Recognition

scholarly journals Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3028 ◽  
Author(s):  
Zina Li ◽  
Lina Qiu ◽  
Ruixin Li ◽  
Zhipeng He ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Average Accuracy

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.

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