LyricFind Corpus

Abstract

<p> Welcome to the LyricFind Corpus, developed at the <a href="https://www.smcnus.org/">Sound &amp; Music Computing laboratory</a> at the National University of Singapore with the very gracious support and partnership of LyricFind, a world leader in legal lyrics licensing and retrieval. </p> <p> In addition to providing the raw data which comprises the analyses in our ISMIR 2015 paper <a href="http://www.smcnus.org/wp-content/uploads/2015/08/LNS_ISMIR2015_116.pdf">“Quantifying Lexical Novelty in Song Lyrics”</a>, we are also pleased to provide 275,905 distinct lyrics in bag-of-words format (67.6 million total word instances), along with identifying lyric, artist, and album IDs that can be cross-referenced with the LyricFind ecosystem. </p> <p> We believe that this dataset marks the largest and cleanest set of lyrics in bag-of-words format yet available, although comparisons with the Million Song Dataset’s musiXmatch lyrics corpus are certainly warranted! </p> <p> The following six files are packaged a single large .zip file, available for download in either .txt. or .xlsx. For more details, please refer to <a href="http://scholarbank.nus.edu.sg/bitstream/10635/137501/1/README.pdf">"README.pdf"</a>. </p> <p> <strong>To reuse this dataset, please ensure the original research paper is cited appropriately.</strong> For details, please refer to Citation Field. </p> <ul> <li>Ellis, R.J., Xing, Z., Fang, J., &amp; Wang, Y. (2015). Quantifying lexical novelty in song lyrics. Proceedings of the 15th International Conference on Music Information Retrieval.</li> </ul> <p> This dataset is also available at <a href="https://www.smcnus.org/lyrics/">https://www.smcnus.org/lyrics/</a> </p>