AUTOMATIC MUSIC EMOTION CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK BASED ON VOCAL AND INSTRUMENTAL SOUND TIMBRES
Mudiana Binti Mokhsin, Nurlaila Binti Rosli, Suzana Zambri, Nor Diana Ahmad and Saidatul Rahah Hamidi
DOI : 10.3844/jcssp.2014.2584.2592
Journal of Computer Science
Volume 10, Issue 12
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.
© 2014 Mudiana Binti Mokhsin, Nurlaila Binti Rosli, Suzana Zambri, Nor Diana Ahmad and Saidatul Rahah Hamidi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.