Abstract:
Music is the effective communication medium
among people. Studying music mood can help in
music understanding, music retrieval, and some
other music-related applications. This paper
presents a hierarchical framework with a new mood
taxonomy model to automate the task of mood
classification from acoustic music data based on
western music psychology theory. This system
proposes hierarchical framework with new mood
taxonomy model. The proposed mood taxonomy
model is combined by the Thayer’s 2 Dimension
model and Schubert’s updated Hevner adjective
checklist. The 60 famous English songs are used as
the standard database in this system which is created
by literature. The verse and chorus part from the
whole song is extracted manually for processing in
this proposed system. The extracted music clip is
segmented by image region growing method to
separate homogenous part on the entire music clip.
Then, the feature sets from the separated music
trimmed are extracted to inject the Fuzzy Support
Vector Machine (SFVM). To solve the multi-label
classification problem, one-against-one (O-A-O)
multi class classification method are used. The
hierarchical framework with new mood taxonomy
model has the advantage of reducing the number of
classifier used for O-A-O approach.
