improving music mood classification using lyrics, audio and social tags

5.2 GROUND TRUTH DATASET
As discussed in Chapters 1 and 2, most previous experiments on music mood classification
were conducted on small experiment datasets, and thus an efficient method is sorely needed for
building ground truth datasets for music mood classification experimentation and evaluation.
This section describes the process of building the experiment dataset for this research.
McKay, McEnnis, and Fujinaga (2006) proposed a list of desired attributes of new music
databases, based on which the author summarizes the following desired characteristics of a
ground truth set for music mood classification:
1) There should be several thousand pieces of music in the ground truth set. Datasets
with hundreds of songs used in previous experiments are too small to draw
generalizable conclusions.
2) The mood categories cover most of the moods expressed by the collection of music
being studied. The three to six categories used in most previous studies
oversimplified the real question.
3) Each of the mood categories should represent a distinctive meaning.
4) One music piece can be labeled with multiple mood categories. This is more realistic
than single-label classification, since a music piece may be “happy and calm,”
“aggressive and depressed,” etc.
5) Each assignment of a mood label to a music piece is validated by multiple human
judges. The more judges, the better it is.
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