Music emotion classification (mec): Exploiting vocal and instrumental sound features

Music emotion classification (mec): Exploiting vocal and instrumental sound features

Music conveys and evokes feeling. Many studies that correlate music with emotion have been done as people nowadays often prefer to listen to a certain song that suits their moods or emotion .This project present works on classifying emotion in music by exploiting vocal and instrumental part of a son...

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Bibliographic Details
Published in:Advances in Intelligent Systems and Computing
Main Author: Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
Format: Article
Language:English
Published: Springer Verlag 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918552567&doi=10.1007%2f978-3-319-07692-8_51&partnerID=40&md5=d5a768d0e6ac49d6d0e8d61201cc670d
id 2-s2.0-84918552567
spelling 2-s2.0-84918552567
Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
Music emotion classification (mec): Exploiting vocal and instrumental sound features
2014
Advances in Intelligent Systems and Computing
287

10.1007/978-3-319-07692-8_51
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918552567&doi=10.1007%2f978-3-319-07692-8_51&partnerID=40&md5=d5a768d0e6ac49d6d0e8d61201cc670d
Music conveys and evokes feeling. Many studies that correlate music with emotion have been done as people nowadays often prefer to listen to a certain song that suits their moods or emotion .This project present works on classifying emotion in music by exploiting vocal and instrumental part of a song. The final system is able to use musical features extracted from vocal part and instrumental part of a song, such as spectral centroid, spectral rolloff and zero-cross as to classify whether selected Malay popular music contain “sad” or “happy” emotion. Fuzzy k-NN (FKNN) and artificial neural network (ANN) are used in this system as a machine classifier. The percentages of emotion classified in Malay popular songs are expected to be higher when both features are applied. © Springer International Publishing Switzerland 2014.
Springer Verlag
21945357
English
Article
author Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
spellingShingle Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
Music emotion classification (mec): Exploiting vocal and instrumental sound features
author_facet Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
author_sort Misron M.M.; Rosli N.; Manaf N.A.; Halim H.A.
title Music emotion classification (mec): Exploiting vocal and instrumental sound features
title_short Music emotion classification (mec): Exploiting vocal and instrumental sound features
title_full Music emotion classification (mec): Exploiting vocal and instrumental sound features
title_fullStr Music emotion classification (mec): Exploiting vocal and instrumental sound features
title_full_unstemmed Music emotion classification (mec): Exploiting vocal and instrumental sound features
title_sort Music emotion classification (mec): Exploiting vocal and instrumental sound features
publishDate 2014
container_title Advances in Intelligent Systems and Computing
container_volume 287
container_issue
doi_str_mv 10.1007/978-3-319-07692-8_51
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918552567&doi=10.1007%2f978-3-319-07692-8_51&partnerID=40&md5=d5a768d0e6ac49d6d0e8d61201cc670d
description Music conveys and evokes feeling. Many studies that correlate music with emotion have been done as people nowadays often prefer to listen to a certain song that suits their moods or emotion .This project present works on classifying emotion in music by exploiting vocal and instrumental part of a song. The final system is able to use musical features extracted from vocal part and instrumental part of a song, such as spectral centroid, spectral rolloff and zero-cross as to classify whether selected Malay popular music contain “sad” or “happy” emotion. Fuzzy k-NN (FKNN) and artificial neural network (ANN) are used in this system as a machine classifier. The percentages of emotion classified in Malay popular songs are expected to be higher when both features are applied. © Springer International Publishing Switzerland 2014.
publisher Springer Verlag
issn 21945357
language English
format Article
accesstype
record_format scopus
collection Scopus
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