چکیده (انگلیسی):

Detecting emotion features in a song remains as a challenge in various area of research especially in Music
Emotion Classification (MEC). In order to classify selected song with certain mood or emotion, the
algorithms of the machine learning must be intelligent enough to learn the data features as to match the
features accordingly to the accurate emotion. Until now, there were only few studies on MEC that exploit
audio timbre features from vocal part of the song incorporated with the instrumental part of a song. Timbre
features is the quality of a musical features or sound that distinguishes different types of sound production
in human voices and musical instruments such as string instruments, wind instruments and percussion
instruments. Most of existing works in MEC are done by looking at audio, lyrics, social tags or combination
of two or more classes. The question is does exploitation of both timbre features from both vocal and
instrumental sound features helped in producing positive result in MEC? Thus, this research present works
on detecting emotion features in Malay popular music using artificial neural network by extracting audio
timbre features from both vocal and instrumental sound clips. The findings of this research will collectively
improve MEC based on the manipulation of vocal and instrumental sound timbre features, as well as
contributing towards the literature of music information retrieval, affective computing and psychology.