improving music mood classification using lyrics, audio and social tags

There are other interesting observations as well. For the hybrid system, the best performance
was achieved with full audio tracks, but the differences were not significant from the
performances using shorter audio clips. The audio-based system, on the other hand, displayed a
different pattern: it performed best when audio length was 60 seconds, and was the worst when
given the entire audio tracks. In fact, more often than not, the beginning and ending parts of a
music track may be quite different from the theme of the song, and thus may convey distracting
and confusing information. However, the reason why the hybrid system worked well with full
audio tracks is left as a topic of future work.
In summary, the answer to research question 5 is positive: combining lyrics and audio can
help reduce the number of training examples and audio length required for achieving certain
performance levels.
8.3 SUMMARY
This chapter described the experiments and results for answering research question 5,
whether combining lyrics and audio help reduce the amount of training data needed for effective
classification. Experiments were conducted to examine the learning curves of the single-sourcebased
systems and the late fusion hybrid system with the best performing lyric feature set
discovered in Chapter 6. The results discovered that complementing audio with lyrics could
reduce the number of training samples required to achieve the same or better performance than
the single-source-based systems.
Another set of experiments were conducted to examine how the length of audio clips would
affect the performances of the audio-based system and the late fusion hybrid system. The results
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