If you ask their shareholders, Spotify is in a great place right now. Ask anyone else, and it's a mess of scams, tone-deaf CEO messaging, and lawsuits. One of the weirdest scams that recently came to light involves (what else) A.I.-generated content. Here's the gist: Covers of popular songs were being inserted into large, publicly available playlists, hidden among dozens of other covers by real artists while racking up millions of listens and getting paid. The artists "performing" the covers--the Highway Outlaws, Waterfront Wranglers, Saltwater Saddles--all fit a certain pattern, with monthly listeners in the hundreds of thousands, zero social media footprint, and some very ChatGPT-sounding bios.

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.

On April 3, 2001, Alanis Morissette and Don Henley appeared before Congress in a bid to save the music industry. Henley, the drummer and a lead vocalist for the Eagles, was dressed in a pin-striped suit. Morissette, the Grammy Award-winning singer of "You Oughta Know," wore a red top and a purple ring. Also present was Hilary Rosen, the president and C.E.O. of the Recording Industry Association of America (R.I.A.A.); Shawn Fanning, the co-founder of Napster; Ken Berry, the president and C.E.O. of EMI Recorded Music; and Dianne Feinstein, the then sixty-seven-year-old senator from California. The Senate Judiciary Committee had called the hearing because online file sharing was understood to be threatening the viability of the entire music industry, and of the future of art in America. As the sole musicians to testify, Morissette and Henley might have chosen to echo the chorus of their record-industry colleagues, bemoaning piracy and praising the R.I.A.A.'s moves to stop it.

The use of community detection algorithms is explored within the framework of cover song identification, i.e. the automatic detection of different audio renditions of the same underlying musical piece. Until now, this task has been posed as a typical query-by-example task, where one submits a query song and the system retrieves a list of possible matches ranked by their similarity to the query. In this work, we propose a new approach which uses song communities to provide more relevant answers to a given query. Starting from the output of a state-of-the-art system, songs are embedded in a complex weighted network whose links represent similarity (related musical content). Communities inside the network are then recognized as groups of covers and this information is used to enhance the results of the system. In particular, we show that this approach increases both the coherence and the accuracy of the system. Furthermore, we provide insight into the internal organization of individual cover song communities, showing that there is a tendency for the original song to be central within the community. We postulate that the methods and results presented here could be relevant to other query-by-example tasks.