improving music mood classification using lyrics, audio and social tags

According to the average accuracies, both hybrid systems outperformed single-source-based
systems. The box plots also show that the late fusion system had the least performance variance
across categories among the four systems and thus was the most stable system. On the other
hand, the hybrid system using feature concatenation seemed the least stable.
Table 7.2 presents the average accuracies of these four systems. It shows that the hybrid
system with late fusion improved accuracy over the audio-only system by 9.6% and 8% for the
top two lyric feature sets respectively. It can also be seen from Table 7.2 that feature
concatenation was not good for combining ANEW + TextStyle lyric feature set and audio, as the
hybrid system using this method performed worse than the lyric-only system (0.629 vs. 0.637).
Table 7.2 Accuracies of single-source-based and hybrid systems
Feature set Audio-only Lyric-only Feature concatenation Late fusion
BEST 0.579 0.638 0.645 0.675
ANEW+TextStyle 0.579 0.637 0.629 0.659
The raw difference of 5.9% between the performances of the lyric-only system and the
audio-only system is noteworthy (Table 7.2). The findings of other researchers (e.g., Laurier et
al., 2008; Mayer et.al, 2008; Yang et.al, 2008; Logan et al., 2004) have never shown lyric-only
systems to outperform audio-only system in terms of averaged accuracy across all categories.
The author surmises that this difference could be because of the new lyric features applied in this
study. However, from Table 7.3 which lists the results of pair-wise statistical tests on system
performances for the top two lyric feature sets, the performance difference between the lyriconly
and audio-only systems was just shy of being accepted as significant (p = 0.054 for the
BEST feature set), and thus more work is needed in the future before this claim could be
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