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.

本文提出了一种利用歌词进行翻唱歌曲检测的新方法，通过引入一份包含 5078 首翻唱歌曲和 2828 首原创歌曲的数据集，并对该方法进行评估和与基准方法的比较，结果表明该方法优于基准方法。

封面歌曲检测中的创新：基于歌词的方法

Innovations in Cover Song Detection: A Lyrics-Based Approach

As online music platforms grow, music recommender systems play a vital role
in helping users navigate and discover content within their vast musical
databases. At odds with this larger goal, is the presence of popularity bias,
which causes algorithmic systems to favor mainstream content over, potentially
more relevant, but niche items. In this work we explore the intrinsic
relationship between music discovery and popularity bias. To mitigate this
issue we propose a domain-aware, individual fairness-based approach which
addresses popularity bias in graph neural network (GNNs) based recommender
systems. Our approach uses individual fairness to reflect a ground truth
listening experience, i.e., if two songs sound similar, this similarity should
be reflected in their representations. In doing so, we facilitate meaningful
music discovery that is robust to popularity bias and grounded in the music
domain. We apply our BOOST methodology to two discovery based tasks, performing
recommendations at both the playlist level and user level. Then, we ground our
evaluation in the cold start setting, showing that our approach outperforms
existing fairness benchmarks in both performance and recommendation of
lesser-known content. Finally, our analysis explains why our proposed
methodology is a novel and promising approach to mitigating popularity bias and
improving the discovery of new and niche content in music recommender systems.

本研究旨在探索在线音乐平台上的流行度偏见问题，并提出了一种基于域感知和个体公平原则的方法来解决流行度偏见对基于图神经网络的音乐推荐系统的影响，以改善对新的和小众内容的发现。通过在冷启动设置下的评估结果表明，我们的方法在性能和推荐较不为人所知的内容方面优于现有的公平基准。