Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli

Classifying emotion in a song remains as a challenge in various area of research. Most of existing work in music emotion classification (MEC) done by looking at features such as audio, lyrics, social tags or combination of two or more features as stated above. There were only few studies on MEC that...

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Main Author:	Rosli, Nurlaila
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Electronic data processing Information technology Knowledge economy Including artificial intelligence and knowledge management Vocal music Instrumental music
Online Access:	https://ir.uitm.edu.my/id/eprint/64250/1/64250.pdf
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id	my-uitm-ir.64250
record_format	uketd_dc
spelling	my-uitm-ir.642502022-09-09T02:34:20Z Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli 2013 Rosli, Nurlaila Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management Vocal music Instrumental music Classifying emotion in a song remains as a challenge in various area of research. Most of existing work in music emotion classification (MEC) done by looking at features such as audio, lyrics, social tags or combination of two or more features as stated above. There were only few studies on MEC that exploit timbre features from vocal part of the song. Thus, this research present works on classifying emotion in music by extracting timbre features from both vocal and instrumental sound clips. Three timbre features, namely spectral centroid, spectral rolloff and zero-cross are extracted based on its attribute in distinguishing between sad audio features and happy audio features. The final system is able to use all of the musical timbre features extracted from vocal part and instrumental part of a song, as to classify the type of emotion in selected Malay popular music. For training and testing purposes, this system is using an Artificial Neural Network (ANN). The percentages of emotion classified in Malay popular songs are projected to be higher when both vocal and instrumental sound features are applied to the ANN classifier. The findings of this research will collectively improve MEC based on manipulation of vocal and instrumental sound timbre features, as well as contributing towards the literature of music information retrieval, affective computing and psychology. However, it is suggested that this research must be incorporated with others features, such as rhythm and spectrum along with timbre features. It is also suggested that other emotion such as anger, calmness, sorrow and etc must be considered for the improvement of this research in the future. 2013 Thesis https://ir.uitm.edu.my/id/eprint/64250/ https://ir.uitm.edu.my/id/eprint/64250/1/64250.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Mokhsin @ Misron, Mudiana
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mokhsin @ Misron, Mudiana
topic	Electronic data processing Information technology Knowledge economy Including artificial intelligence and knowledge management Vocal music Instrumental music
spellingShingle	Electronic data processing Information technology Knowledge economy Including artificial intelligence and knowledge management Vocal music Instrumental music Rosli, Nurlaila Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
description	Classifying emotion in a song remains as a challenge in various area of research. Most of existing work in music emotion classification (MEC) done by looking at features such as audio, lyrics, social tags or combination of two or more features as stated above. There were only few studies on MEC that exploit timbre features from vocal part of the song. Thus, this research present works on classifying emotion in music by extracting timbre features from both vocal and instrumental sound clips. Three timbre features, namely spectral centroid, spectral rolloff and zero-cross are extracted based on its attribute in distinguishing between sad audio features and happy audio features. The final system is able to use all of the musical timbre features extracted from vocal part and instrumental part of a song, as to classify the type of emotion in selected Malay popular music. For training and testing purposes, this system is using an Artificial Neural Network (ANN). The percentages of emotion classified in Malay popular songs are projected to be higher when both vocal and instrumental sound features are applied to the ANN classifier. The findings of this research will collectively improve MEC based on manipulation of vocal and instrumental sound timbre features, as well as contributing towards the literature of music information retrieval, affective computing and psychology. However, it is suggested that this research must be incorporated with others features, such as rhythm and spectrum along with timbre features. It is also suggested that other emotion such as anger, calmness, sorrow and etc must be considered for the improvement of this research in the future.
format	Thesis
qualification_level	Master's degree
author	Rosli, Nurlaila
author_facet	Rosli, Nurlaila
author_sort	Rosli, Nurlaila
title	Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
title_short	Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
title_full	Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
title_fullStr	Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
title_full_unstemmed	Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli
title_sort	music emotion classification based on vocal and instrumental sound features using artificial neural network / nurlaila rosli
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2013
url	https://ir.uitm.edu.my/id/eprint/64250/1/64250.pdf
_version_	1783735425956839424

Music emotion classification based on vocal and instrumental sound features using artificial neural network / Nurlaila Rosli

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