Lyricist-Singer Entropy Affects Lyric-Lyricist Classification
  Performance
Mitsuki Morita Masato Kikuchi Tadachika Ozono
Although lyrics represent an essential component of music, few music
information processing studies have been conducted on the characteristics of
lyricists. Because these characteristics may be valuable for musical
applications, such as recommendations, they warrant further study. We
considered a potential method that extracts features representing the
characteristics of lyricists from lyrics. Because these features must be
identified prior to extraction, we focused on lyricists with easily
identifiable features. We believe that it is desirable for singers to perform
unique songs that share certain characteristics specific to the singer.
Accordingly, we hypothesized that lyricists account for the unique
characteristics of the singers they write lyrics for. In other words,
lyric-lyricist classification performance or the ease of capturing the features
of a lyricist from the lyrics may depend on the variety of singers. In this
study, we observed a relationship between lyricist-singer entropy or the
variety of singers associated with a single lyricist and lyric-lyricist
classification performance. As an example, the lyricist-singer entropy is
minimal when the lyricist writes lyrics for only one singer. In our
experiments, we grouped lyricists among five groups in terms of lyricist-singer
entropy and assessed the lyric-lyricist classification performance within each
group. Consequently, the best F1 score was obtained for the group with the
lowest lyricist-singer entropy. Our results suggest that further analyses of
the features contributing to lyric-lyricist classification performance on the
lowest lyricist-singer entropy group may improve the feature extraction task
for lyricists.