This paper is published in Volume-4, Issue-2, 2018
Area
Signal Processing
Author
S. Kumari
Co-authors
U. Kowsalya, R. Preethi, R. Theepa, J. Edward Paulraj, S. JeyaAnusuya
Org/Univ
T. J. S. Engineering College, Puduvoyal, Tamil Nadu, India
Pub. Date
26 March, 2018
Paper ID
V4I2-1374
Publisher
Keywords
Speech Emotion Recognition, Classification, Convolutional Neural Networks, CNN, Deep Belief Networks, DBN.

Citationsacebook

IEEE
S. Kumari, U. Kowsalya, R. Preethi, R. Theepa, J. Edward Paulraj, S. JeyaAnusuya. Audio-Visual Emotion Recognition using 3DCNN and DBN Techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
S. Kumari, U. Kowsalya, R. Preethi, R. Theepa, J. Edward Paulraj, S. JeyaAnusuya (2018). Audio-Visual Emotion Recognition using 3DCNN and DBN Techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.

MLA
S. Kumari, U. Kowsalya, R. Preethi, R. Theepa, J. Edward Paulraj, S. JeyaAnusuya. "Audio-Visual Emotion Recognition using 3DCNN and DBN Techniques." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.

Abstract

Emotion recognition is difficult because of the emotional hole amongst emotions and varying audio-visual highlights. Propelled by the effective element learning capacity of profound neural networks, this system proposes to connect the emotional hole by utilizing a hybrid deep replication, which first creates varying audio-visual fragment highlights with Convolutional Neural Networks (CNN) and 3DCNN, at that point wires varying audio-visual section includes in a Deep Belief Networks (DBN). The point of this postulation work is to research the algorithm of discourse Emotion recognition utilizing MATLAB. Right off the bat, five most generally utilized highlights are chosen and separated from discourse flag. After this, measurable esteems, for example, mean, change will be gotten from the highlights. This information alongside their related Emotion target will be bolstered to MATLAB neural network apparatus to train and test to make up the classifier. The general framework gives a solid execution, arranging effectively over 82% discourse tests after appropriately preparing.
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