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.

翻译：尽管歌词是音乐的重要组成元素，但鲜有音乐信息处理研究关注作词人的特征。由于这些特征可能对推荐等音乐应用具有价值，值得进一步探究。我们考虑一种从歌词中提取作词人特征表征的潜在方法。由于这些特征必须在提取前识别，我们聚焦于特征易于识别的作词人。我们认为歌手演绎具有特定共同特征的独特歌曲是可取的。据此推断，作词人会考虑其创作歌词的歌手所独有的特征。换言之，歌词-作词人分类性能（即从歌词中捕捉作词人特征的难易程度）可能取决于歌手的多样性。本研究观察到作词人-歌手熵（即单个作词人关联的歌手多样性）与歌词-作词人分类性能之间存在关联。例如，当作词人仅为单一歌手创作歌词时，作词人-歌手熵最小。实验中，我们根据作词人-歌手熵将作词人分为五组，评估各组的歌词-作词人分类性能。结果表明，作词人-歌手熵最低的组别获得了最佳F1分数。我们的发现表明，针对最低熵组别中影响歌词-作词人分类性能的特征进行深入分析，或可改进作词人特征提取任务。