improving music mood classification using lyrics, audio and social tags

Figure 8.1 shows a general trend that all system performances increased with more training
data, but the performance of the audio-based system increased much more slowly than the other
systems. With 20% training samples, the accuracies of the hybrid and the lyric-only systems
were already better than the highest accuracy of the audio-only system with all possible amounts
of training data. To achieve similar accuracy, the hybrid system needed about 20% fewer training
examples than the lyric-only system. This validates the hypothesis that combining lyrics and
audio can reduce required training examples needed to achieve certain classification performance
levels. In addition, the learning curve of the audio-only system levels off (i.e., stops increasing)
at 80% training sample size, while the curves of the other two systems never level off. This
indicates the hybrid system and lyric-only system may further improve their performances if
given more training examples. It is also worthy of notice that the performances of the lyric-only
and audio-only systems drop at the points of 40% and 70% training examples respectively. This
observation seems to contradict the general trend that performance increases with the amount of
available training data. However, the performance differences between these points and their
neighboring points are not statistically significant (at p < 0.05), and thus these performance drops
can be seen as random effects and do not form a counter case of the general trend of the learning
curves.
8.2 AUDIO LENGTHS
The second part of research question 5 is about the effect of audio lengths on classification
performance, and whether incorporating lyrics can reduce the requirement on the length of audio
data for achieving certain performance levels. This research compares the performances of the
audio-based, lyric-based and the late fusion hybrid system on datasets with audio clips of various
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