Kara1k: A Karaoke Dataset for Cover Song Identification and Singing Voice Analysis

Abstract : We introduce Kara1k, a new musical dataset composed of 2,000 analyzed songs thanks to a partnership with a karaoke company. The dataset is divided into 1,000 cover songs provided by Recisio Karafun application, and the corresponding 1,000 songs by the original artists. Kara1k is mainly dedicated toward cover song identification and singing voice analysis. For both tasks, it offers novel approaches, as each cover song is a studio-recorded song with the same arrangement as the original recording, but with different singers and musicians. Essentia, harmony-analyser, Marsyas, Vamp plugins and YAAFE have been used to extract audio features for each track in Kara1k. We provide metadata such as the title, genre, original artist, year, International Standard Recording Code and the ground truths for the singer's gender, backing vocals, duets and lyrics' language. Additionally, we provide the instrumental track and the pure singing voice track for each cover song. We showcase two use-case experiments for Kara1k. In the cover song identification task using the Dynamic Time Warping method, we provide a comparison of traditional and new features: chroma and MFCC features, chords and keys, and chroma and chord distances. We obtain 84-89% identification accuracy for three of the features, which justifies our focus on karaoke songs. In the supporting experiment on singer gender classification, we evaluate the difference in the performance in two conditions - a pure singing voice and the singing voice mixed with the background music. The Kara1k dataset is freely available under the KaraMIR project website.
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Contributor : Yann Bayle <>
Submitted on : Tuesday, April 10, 2018 - 2:21:08 PM
Last modification on : Saturday, June 1, 2019 - 11:34:02 AM




Yann Bayle, Ladislav Marsik, Martin Rusek, Matthias Robine, Pierre Hanna, et al.. Kara1k: A Karaoke Dataset for Cover Song Identification and Singing Voice Analysis. 2017 IEEE International Symposium on Multimedia (ISM), Dec 2017, Taichung, Taiwan. ⟨10.1109/ISM.2017.32⟩. ⟨hal-01762806⟩



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