Artist Attribution via Song Lyrics

Song lyrics, separated from the audio signal of their song, still contain a significant amount of information. Mood and meaning can still be conveyed effectively by a pure textual representation. There has even been somewhat successful previous work on genre classification from song lyrics[7]. Building on previous work, we seek to build an artist attribution system for song lyrics. This task is in the same vein as classic author attribution tasks, which often are trained and evaluated on extremely large datasets[8]; providing more data per author than it is possible to get for most songwriters. In order to focus our task, we focus only on rap, as the songwriter and performer are usually the same, and there is a heavy emphasis on distinctive forms of lyricism. We actually enshrine that first assumption in our statement of the classification task: given a textual representation of the lyrics of a rap, return the name of the artist who raps it. This is a limitation we will have to live with for now, there does not exist any large public database that provides ghostwriting information for rappers. Potential use cases for such a classifier would be for detecting misattributed songs in a music library or as part of an auto-tagger in a music management system, along with other uses of author-attribution systems. A lyric-only classifier could also be used in an ensemble method that includes audio-only classifiers. Following previous work[2], we initially attempt to distinguish between 4 prolific rappers (Eminem, Nas, Jay Z, and Nicki Minaj) before expanding the classification task to encompass more artists, testing thoroughly on a 12-artist dataset, and eventually testing on over 300 rappers at once.