Human emotion recognition and analysis in response to audio music using brain signals.
Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, ra...
| Publicado en: | Computers in Human Behavior Vol. 65; pp. 267 - 276 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Dec2016
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=118739860&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 118739860 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Dec2016 vid: 65 pid: 2410 pub: Elsevier B.V. artinfo: ui: 118739860 10.1016/j.chb.2016.08.029 ppf: 267 ppct: 9 formats: tig: atl: Human emotion recognition and analysis in response to audio music using brain signals. aug: au: Bhatti, Adnan Mehmood Majid, Muhammad Anwar, Syed Muhammad Khan, Bilal affil: Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan Department of Software Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, 22010, Pakistan su: Age distribution Emotions Music Electroencephalography Experimental design sug: subj: Age distribution Emotions Music Electroencephalography Experimental design keyword: Classification Electroencephalography (EEG) Feature extraction Machine learning Classification Electroencephalography (EEG) Feature extraction Machine learning ab: Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, rap, metal, rock and hiphop genres. Participants were asked to listen to audio music tracks of 1 min for each genre in a noise free environment. The main objectives of this study were to determine the effect of different genres of music on human emotions and indicating age group that is more responsive to music. Thirty men and women of three different age groups (15–25 years, 26–35 years and 36–50 years) underwent through the experiment that also included self reported emotional state after listening to each type of music. Features from three different domains i.e., time, frequency and wavelet were extracted from recorded EEG signals, which were further used by the classifier to recognize human emotions. It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals. It is also observed that rock and rap genres generated happy and sad emotions respectively in subjects under study. The brain signals of age group (26–35 years) gave best emotion recognition accuracy in accordance to the self reported emotions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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