Laughter classification using 3D convolutional neural networks
Social signals express the attitude of human being in social situations. Laughter has been determined as an important social signal that can predict emotional information of people. It conveys different emotions such as happiness, surprise, fear, anger, and anxiety. Therefore, identifying and extrac...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2019
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/3026 |
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| Institution: | De La Salle University |
| Summary: | Social signals express the attitude of human being in social situations. Laughter has been determined as an important social signal that can predict emotional information of people. It conveys different emotions such as happiness, surprise, fear, anger, and anxiety. Therefore, identifying and extracting emotions in the laughter is useful for estimating the emotional state of the user. Deep neural networks are replacing traditional methods because they perform more accurately. This paper presents work that detects the emotions in laughter by using audio features and running 3D Convolutional Neural Networks. The best rate of accuracy produced by 3D CNNs is 97.97%, which is higher than the results of our previous paper, which applied MLP and SVM on Iranian laughter dataset. © 2019 ACM. |
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