dc.contributor.authorTan, Kian Tong.
dc.date.accessioned2009-06-18T02:11:19Z
dc.date.available2009-06-18T02:11:19Z
dc.date.copyright2009en_US
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10356/17931
dc.description.abstractWith high popularity of audio files and increasing size of data storages devices, organizing audio files became a huge problem. In order to provide a better solution of storing and classifying music, this project will create a program that will automatically classify the music according to the mood that is usually perceived by the listeners. Before starting on how to classify music moods, one would need to know what the music moods are and how are they is going to be classified. It is found that Hevner’s classification of music moods is one of the ways that can classify the music moods. According to Hevner’s classification, there are eight major music mood categories. All the music emotions are classified under these eight groups. In this project, statistical approach is being used towards automatic classification of music moods. As a result, data about the song needed to be gathered. This is being done by using MATLAB and MIR toolbox to help in extracting the music features and the data. Data being mainly mean and standard deviation of each music feature. A total 76 different data feature are obtained. The song database is divided in to two, 320 training files and 160 testing files. In the end, only 71.25% of accuracy is obtained in classifying 4 groups.en_US
dc.format.extent85 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineeringen_US
dc.titleAutomatic music mood classificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWan Chunruen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeELECTRICAL and ELECTRONIC ENGINEERINGen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record