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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Ataollahi, Faramarz, Suarez, Merlin Teodosia
Format:	text
Published:	Animo Repository 2019
Subjects:	Emotion recognition Laughter Neural networks (Computer science) Computer Sciences Software Engineering
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/3026
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-4025
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-40252021-11-22T00:36:05Z Laughter classification using 3D convolutional neural networks Ataollahi, Faramarz Suarez, Merlin Teodosia 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. 2019-10-26T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/3026 Faculty Research Work Animo Repository Emotion recognition Laughter Neural networks (Computer science) Computer Sciences Software Engineering
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Emotion recognition Laughter Neural networks (Computer science) Computer Sciences Software Engineering
spellingShingle	Emotion recognition Laughter Neural networks (Computer science) Computer Sciences Software Engineering Ataollahi, Faramarz Suarez, Merlin Teodosia Laughter classification using 3D convolutional neural networks
description	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.
format	text
author	Ataollahi, Faramarz Suarez, Merlin Teodosia
author_facet	Ataollahi, Faramarz Suarez, Merlin Teodosia
author_sort	Ataollahi, Faramarz
title	Laughter classification using 3D convolutional neural networks
title_short	Laughter classification using 3D convolutional neural networks
title_full	Laughter classification using 3D convolutional neural networks
title_fullStr	Laughter classification using 3D convolutional neural networks
title_full_unstemmed	Laughter classification using 3D convolutional neural networks
title_sort	laughter classification using 3d convolutional neural networks
publisher	Animo Repository
publishDate	2019
url	https://animorepository.dlsu.edu.ph/faculty_research/3026
_version_	1718383341221183488

Laughter classification using 3D convolutional neural networks

Similar Items