A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

The popularity of the Internet has brought the rapid development of artificial intelligence, affective computing, Internet of things (IoT), and other technologies. Particularly, the development of IoT provides more references for the realization of smart home. However, when people have achieved a ce...

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Autores principales:	I.-Sheng Huang, Yu-Hsuan Lu, Muhammad Shafiq, Asif Ali Laghari, Rahul Yadav
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/e3fde83caaee4bc28b557c079d9d6f03
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id	oai:doaj.org-article:e3fde83caaee4bc28b557c079d9d6f03
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spelling	oai:doaj.org-article:e3fde83caaee4bc28b557c079d9d6f032021-11-15T01:18:58ZA Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT1875-905X10.1155/2021/3561829https://doaj.org/article/e3fde83caaee4bc28b557c079d9d6f032021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3561829https://doaj.org/toc/1875-905XThe popularity of the Internet has brought the rapid development of artificial intelligence, affective computing, Internet of things (IoT), and other technologies. Particularly, the development of IoT provides more references for the realization of smart home. However, when people have achieved a certain amount of material satisfaction, they are more likely to want to communicate emotionally. Music contains a lot of emotion information. Music data is an important communication way between people and a better way to convey emotions. Therefore, it has become one of the most convenient and natural interactive ways expected by people in intelligent human-computer interaction. Traditional music emotion recognition methods have some demerits such as low recognition rate and time-consuming. So, we propose a generative adversarial network (GAN) model based on intelligent data analytics for music emotion recognition under IoT. Driven by the double-channel fusion strategy, the GAN can effectively extract the local and global features of the image or voice. Meanwhile, in order to increase the feature difference between the emotional voices, the feature data matrix of the Meyer frequency cepstrum coefficient of the music signals is transformed to improve the expression ability of the GAN. The experiment results show that the proposed model can effectively recognize the music emotion. Compared with other state-of-the-art approaches, the error recognition rate of proposed music music data recognition is greatly reduced. In terms of the accuracy, it exceeds 87% which is higher than that of other methods.I.-Sheng HuangYu-Hsuan LuMuhammad ShafiqAsif Ali LaghariRahul YadavHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 I.-Sheng Huang Yu-Hsuan Lu Muhammad Shafiq Asif Ali Laghari Rahul Yadav A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
description	The popularity of the Internet has brought the rapid development of artificial intelligence, affective computing, Internet of things (IoT), and other technologies. Particularly, the development of IoT provides more references for the realization of smart home. However, when people have achieved a certain amount of material satisfaction, they are more likely to want to communicate emotionally. Music contains a lot of emotion information. Music data is an important communication way between people and a better way to convey emotions. Therefore, it has become one of the most convenient and natural interactive ways expected by people in intelligent human-computer interaction. Traditional music emotion recognition methods have some demerits such as low recognition rate and time-consuming. So, we propose a generative adversarial network (GAN) model based on intelligent data analytics for music emotion recognition under IoT. Driven by the double-channel fusion strategy, the GAN can effectively extract the local and global features of the image or voice. Meanwhile, in order to increase the feature difference between the emotional voices, the feature data matrix of the Meyer frequency cepstrum coefficient of the music signals is transformed to improve the expression ability of the GAN. The experiment results show that the proposed model can effectively recognize the music emotion. Compared with other state-of-the-art approaches, the error recognition rate of proposed music music data recognition is greatly reduced. In terms of the accuracy, it exceeds 87% which is higher than that of other methods.
format	article
author	I.-Sheng Huang Yu-Hsuan Lu Muhammad Shafiq Asif Ali Laghari Rahul Yadav
author_facet	I.-Sheng Huang Yu-Hsuan Lu Muhammad Shafiq Asif Ali Laghari Rahul Yadav
author_sort	I.-Sheng Huang
title	A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
title_short	A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
title_full	A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
title_fullStr	A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
title_full_unstemmed	A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT
title_sort	generative adversarial network model based on intelligent data analytics for music emotion recognition under iot
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/e3fde83caaee4bc28b557c079d9d6f03
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A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

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