improving music mood classification using lyrics, audio and social tags

There are two popular approaches in assembling hybrid systems (also called “fusion
methods”). The most straightforward one is feature concatenation where two feature sets are
concatenated and the classification algorithms run on the combined feature vectors (e.g. Laurier
et al., 2008; Mayer et al., 2008). The other method is often called “late fusion” which combines
the outputs of individual classifiers based on different sources, either by (weighted) averaging
(e.g., Bischoff et al., 2009b; Whitman & Smaragdis, 2002) or by multiplying (e.g., Li & Ogihara,
2004). To answer this research question, both fusion methods are implemented, and the
performances of hybrid systems and systems based on single sources are compared using
statistical tests.
1.3.5 Learning Curves and Audio Lengths
A learning curve describes the relationship between classification performance and the
number of training examples. Usually performance increases with the number of training
examples, and the point where performance stops increasing indicates the minimum number of
training examples needed for achieving the best performance. In addition to classification
performances, the learning curve is also an important measure for the effectiveness of a
classification system. Therefore, the comparison on learning curves of the hybrid systems and
single-source-based systems can reveal whether combining lyrics and audio helps reduce the
number of training examples needed for achieving comparable or better performances as audioonly
or lyric-only systems.
Due to the time complexity of audio processing, MIR systems often process the x second
audio clips truncated from the middle of the original tracks instead of the complete tracks, where
x often equals 30 or 15. As text processing is much faster than audio processing, it is of practical
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