Innovations in Cover Song Detection: A Lyrics-Based Approach
This addresses the challenge of identifying cover songs in online music platforms, which is important for music culture and industry applications, but it is incremental as it builds on existing detection methods by focusing on lyrics.
The paper tackles the problem of cover song detection by proposing a novel lyrics-based method, and it shows that this method outperforms baseline approaches on a new dataset containing 5078 cover songs and 2828 original songs.
Cover songs are alternate versions of a song by a different artist. Long being a vital part of the music industry, cover songs significantly influence music culture and are commonly heard in public venues. The rise of online music platforms has further increased their prevalence, often as background music or video soundtracks. While current automatic identification methods serve adequately for original songs, they are less effective with cover songs, primarily because cover versions often significantly deviate from the original compositions. In this paper, we propose a novel method for cover song detection that utilizes the lyrics of a song. We introduce a new dataset for cover songs and their corresponding originals. The dataset contains 5078 cover songs and 2828 original songs. In contrast to other cover song datasets, it contains the annotated lyrics for the original song and the cover song. We evaluate our method on this dataset and compare it with multiple baseline approaches. Our results show that our method outperforms the baseline approaches.