improving music mood classification using lyrics, audio and social tags

Support Vector Machines (SVM) as its classification model. Specifically, it integrated the
LIBSVM (Chang & Lin, 2001) implementation with a linear kernel to build the classifiers.
7.2 HYBRID METHODS
Hybrid methods can be used to flexibly integrate heterogeneous data sources to improve
classification performance, and they work best when the sources are sufficiently diverse and thus
can possibly make up for each other's mistakes. Previous work in music classification has used
such hybrid sources as audio and social tags, audio and lyrics, audio and symbolic representation
of scores, etc.
7.2.1 Two Hybrid Methods
Previous work in music classification has used two popular hybrid methods to combine
multiple information sources. The most straightforward hybrid method is feature concatenation
where two feature sets are concatenated and the classification algorithms run on the combined
feature vectors. The other method is often called “late fusion” which is to combine the outputs of
individual classifiers based on different sources, either by (weighted) averaging or by
multiplying.
According to Tax, van Breukelen, Duin and Kittler (2000), in the case of combining two
classifiers for binary classification as in this research, the two late fusion variations, averaging
and multiplying are essentially the same. The following is a formal proof of this assertion.
Lemma: For combining the outputs of two classifiers (lyric-based and audio-based) for
binary classification, the rules of multiplying and averaging are equivalent.
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