Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance

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. Consequently, 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 the present 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. We expected lyricists with small lyricist-singer entropies to be easily classifiable. To verify our hypothesis, we conducted the following experiments. First, we grouped lyricists among five groups in terms of lyricist-singer entropy and assessed the lyric-lyricist classification performance within each group. Subsequently, we statistically evaluated the relationship between lyricist-singer entropy and lyric-lyricist classification performance, finding a weak negative correlation that supports our hypothesis. Specifically, 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.