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The WASABI song corpus and knowledge graph for music lyrics analysis

Abstract

We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. The corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and recommendation of songs. We demonstrate the utility and versatility of the WASABI Song Corpus in three concrete application scenarios. Together with the work on the corpus, we present the work achieved to transition the dataset into a knowledge graph, the WASABI RDF Knowledge Graph, and we show how this will enable an even richer set of applications.

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Notes

  1. http://www.semanticaudio.ac.uk.

  2. http://wasabi.i3s.unice.fr/.

  3. https://en.wikipedia.org/wiki/LyricWiki.

  4. from http://usdb.animux.de/.

  5. http://millionsongdataset.com/lastfm/.

  6. Based on language detection performed on the lyrics.

  7. We take the genre of the album as ground truth since song-wise genres are much rarer.

  8. We take the album publication date as proxy since song-wise labels are too sparse.

  9. http://wasabi.i3s.unice.fr/.

  10. https://wasabi.i3s.unice.fr/apidoc/.

  11. http://wasabi.inria.fr/sparql.

  12. https://doi.org/10.5281/zenodo.5603369.

  13. https://en.wikipedia.org/wiki/LyricWiki.

  14. https://developer.musixmatch.com/, here is a sample code for lyrics retrieval using this API at https://jsbin.com/joyifojuva/edit.

  15. https://wasabi.i3s.unice.fr/#/search/artist/Britney%20Spears/album/In%20The%20Zone/song/Everytime.

  16. Obtained f-scores ranged between 70.8% for text-based and 75.3% for text-audio-based models.

  17. In our segmentation experiments we found this simple metric to outperform more complex metrics that take into account the phonetics or the syntax.

  18. https://github.com/TuringTrain/lyrics_thumbnailing.

  19. Labels provided by Deezer. Furthermore, 625k songs have a different status such as unknown or censored version.

  20. Sometimes, a third dimension of dominance is part of the model.

  21. https://github.com/deezer/deezer_mood_detection_dataset.

  22. Depiction without scatter plot taken from Parisi et al. (2019)

  23. made with https://www.wortwolken.com/.

  24. The software can be downloaded at https://spacy.io/. We used the large model en_core_web_trf which is based on a transformers architecture.

  25. Note that in this Fig. we only show the artists with the most connections. Most connections from Bob Dylan are not visible as they are connected to not visualized nodes.

  26. http://babelfy.org.

  27. https://github.com/micbuffa/WasabiDataset/.

  28. SCOT (Social Semantic Cloud of Tags) Ontology: http://rdfs.org/scot/spec/.

  29. Audio Features Ontology: http://purl.org/ontology/af/.

  30. OMRAS2 Chord Ontology: http://purl.org/ontology/chord/.

  31. http://rdfs.org/scot/spec/.

  32. http://www.ontologydesignpatterns.org/ont/dul/ontopic.owl.

  33. Version 1: https://doi.org/10.5281/zenodo.4312641, version 2: https://doi.org/10.5281/zenodo.5603369.

  34. http://wasabi.inria.fr/sparql.

  35. http://millionsongdataset.com.

  36. http://millionsongdataset.com/musixmatch/.

  37. https://www.soundtrackyourbrand.com.

References

  • Adamou, A., Brown, S., Barlow, H., Allocca, C., & d’Aquin, M. (2019). Crowdsourcing linked data on listening experiences through reuse and enhancement of library data. International Journal on Digital Libraries, 20(1), 61–79.

    Article  Google Scholar 

  • Allik, A., Thalmann, F., & Sandler, M. (2018). MusicLynx: Exploring music through artist similarity graphs. In: Companion Proceedings (Dev. Track) The Web Conference (WWW 2018)

  • Atherton, J., & Kaneshiro, B. (2016). I said it first: Topological analysis of lyrical influence networks. In: ISMIR, pp. 654–660

  • Baratè, A., Ludovico, L.A., & Santucci, E. (2013). A semantics-driven approach to lyrics segmentation. In: 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization, pp. 73–79. https://doi.org/10.1109/SMAP.2013.15

  • Bergelid, L. (2018). Classification of explicit music content using lyrics and music metadata

  • Berthelon, F., & Sander, P. (2013). Emotion Ontology for Context Awareness. In: Coginfocom 2013 - 4th IEEE Conference on Cognitive Infocommunicaitons. Budapest, Hungary. https://hal.archives-ouvertes.fr/hal-00908543

  • Bertin-Mahieux, T., Ellis, D.P., Whitman, B., & Lamere, P. (2011). The million song dataset. In: Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011)

  • Bhatia, S., Lau, J.H., & Baldwin, T. (2016). Automatic labelling of topics with neural embeddings. arXiv preprint arXiv:1612.05340

  • Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022

  • Brackett, D. (1995). Interpreting Popular Music. Cambridge University Press. https://books.google.fr/books?id=yHniAAAAMAAJ

  • Buffa, M., Cabrio, E., Fell, M., Gandon, F., Giboin, A., Hennequin, R., Michel, F., Pauwels, J., Pellerin, G., Tikat, M., & Winckler, M. (2021) The WASABI dataset: Cultural, lyrics and audio analysis metadata about 2 million popular commercially released songs. In: Proceedings of ESWC 2021 (to be published)

  • Buffa, M., & Lebrun, J. (2017a). Real time tube guitar amplifier simulation using webaudio. In: Proceedings 3rd Web Audio Conference (WAC 2017)

  • Buffa, M., & Lebrun, J. (2017b). Web audio guitar tube amplifier vs native simulations. In: Proceedings 3rd Web Audio Conference (WAC 2017)

  • Buffa, M., Lebrun, J., Kleimola, J., & Letz, S., et al. (2018). Towards an open web audio plugin standard. In: Companion Proceedings of the The Web Conference 2018, pp. 759–766. International World Wide Web Conferences Steering Committee

  • Buffa, M., Lebrun, J., Pauwels, J., & Pellerin, G. (2019a). A 2 Million Commercial Song Interactive Navigator. In: WAC 2019 - 5th WebAudio Conference 2019. Trondheim, Norway. https://hal.inria.fr/hal-02366730

  • Buffa, M., Lebrun, J., Pellerin, G., & Letz, S. (2019b). Webaudio plugins in daws and for live performance. In: 14th International Symposium on Computer Music Multidisciplinary Research (CMMR’19)

  • Çano, E., & Morisio, M. (2017). Music mood dataset creation based on last.fm tags. In: 2017 International Conference on Artificial Intelligence and Applications, Vienna Austria. https://doi.org/10.5121/csit.2017.70603

  • Chatterjee, A., Narahari, K.N., Joshi, M., & Agrawal, P. (2019). Semeval-2019 task 3: Emocontext contextual emotion detection in text. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 39–48

  • Chin, H., Kim, J., Kim, Y., Shin, J., & Yi, M.Y. (2018). Explicit content detection in music lyrics using machine learning. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 517–521. IEEE

  • Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J., & Moussallam, M. (2018). Music mood detection based on audio and lyrics with deep neural net. arXiv preprint arXiv:1809.07276

  • Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  • Fell, M. (2014). Lyrics classification. In: Master’s thesis, Saarland University, Germany, 2014.

  • Fell, M. (2020). Natural language processing for music information retrieval: Deep analysis of lyrics structure and content. Theses, Université Côte d’Azur. https://tel.archives-ouvertes.fr/tel-02587910

  • Fell, M., Cabrio, E., Corazza, M., & Gandon, F. (2019). Comparing Automated Methods to Detect Explicit Content in Song Lyrics. In: RANLP 2019 - Recent Advances in Natural Language Processing. Varna, Bulgaria. https://hal.archives-ouvertes.fr/hal-02281137

  • Fell, M., Cabrio, E., Gandon, F., & Giboin, A. (2019). Song lyrics summarization inspired by audio thumbnailing. In: RANLP 2019 - Recent Advances in Natural Language Processing (RANLP). Varna, Bulgaria. https://hal.archives-ouvertes.fr/hal-02281138

  • Fell, M., Cabrio, E., Korfed, E., Buffa, M., & Gandon, F. (2020). Love me, love me, say (and write!) that you love me: Enriching the WASABI song corpus with lyrics annotations. In: Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020, pp. 2138–2147. https://www.aclweb.org/anthology/2020.lrec-1.262/

  • Fell, M., Nechaev, Y., Cabrio, E., & Gandon, F. (2018). Lyrics Segmentation: Textual Macrostructure Detection using Convolutions. In: Conference on Computational Linguistics (COLING), pp. 2044–2054. Santa Fe, New Mexico, United States. https://hal.archives-ouvertes.fr/hal-01883561

  • Fell, M., Sporleder, C.: Lyrics-based analysis and classification of music. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 620–631 (2014)

  • Fell, M., Yaroslav, N., Gabriel, M.B., Cabrio, E., Gandon, F., & Peeters, G. (2021). Lyrics segmentation via bimodal text-audio representation. Natural Language Engineering ( to appear)

  • Fillon, T., Simonnot, J., Mifune, M.F., Khoury, S., Pellerin, G., & Le Coz, M. (2014). Telemeta: An open-source web framework for ethnomusicological audio archives management and automatic analysis. In: Proceedings of the 1st International Workshop on Digital Libraries for Musicology, pp. 1–8. ACM

  • Hennequin, R., Khlif, A., Voituret, F., & Moussallam, M. (2019). Spleeter: A fast and state-of-the art music source separation tool with pre-trained models. Late-Breaking/Demo ISMIR 2019. Deezer Research

  • Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength Natural Language Processing in Python. https://doi.org/10.5281/zenodo.1212303

  • Hu, X., Downie, J.S., & Ehmann, A.F. (2009). Lyric text mining in music mood classification. American music 183(5,049), 2–209

  • Hu, Y., Chen, X., & Yang, D. (2009). Lyric-based song emotion detection with affective lexicon and fuzzy clustering method. In: ISMIR

  • Kim, J., & Mun, Y.Y. (2019). A hybrid modeling approach for an automated lyrics-rating system for adolescents. In: European Conference on Information Retrieval, pp. 779–786. Springer

  • Kleedorfer, F., Knees, P., & Pohle, T. (2008). Oh oh oh whoah! towards automatic topic detection in song lyrics. In: ISMIR

  • Lisena, P., Achichi, M., Choffé, P., Cecconi, C., Todorov, K., Jacquemin, B., & Troncy, R. (2018). Improving (re-) usability of musical datasets: An overview of the doremus project. Bibliothek Forschung und Praxis, 42(2), 194–205.

    Article  Google Scholar 

  • Logan, B., Kositsky, A., & Moreno, P. (2004). Semantic analysis of song lyrics. In: 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), vol. 2, pp. 827–830 Vol.2. https://doi.org/10.1109/ICME.2004.1394328

  • Mahedero, J.P.G., Martínez, A., Cano, P., Koppenberger, M., & Gouyon, F. (2005). Natural language processing of lyrics. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, MULTIMEDIA ’05, pp. 475–478. ACM, New York, NY, USA. http://doi.acm.org/10.1145/1101149.1101255

  • Meroño-Peñuela, A., Hoekstra, R., Gangemi, A., Bloem, P., de Valk, R., Stringer, B., Janssen, B., de Boer, V., Allik, A., Schlobach, S., et al.: The midi linked data cloud. In: International Semantic Web Conference, pp. 156–164. Springer (2017)

  • Meseguer-Brocal, G., Peeters, G., Pellerin, G., Buffa, M., Cabrio, E., Faron Zucker, C., Giboin, A., Mirbel, I., Hennequin, R., Moussallam, M., Piccoli, F., & Fillon, T. (2017). WASABI: A Two Million Song Database Project with Audio and Cultural Metadata plus WebAudio enhanced Client Applications. In: Web Audio Conference 2017 – Collaborative Audio #WAC2017. Queen Mary University of London, London, United Kingdom

  • Buffa, M., Tikat, M., & M.W. (2021). Interactive multimedia visualization for exploring and fixing a multi-dimensional metadata base of popular musics. In: Proceedings of the MEPDaW Workshop, ISWC

  • Mihalcea, R., & Strapparava, C. (2012). Lyrics, music, and emotions. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 590–599. Association for Computational Linguistics, Jeju Island, Korea . https://www.aclweb.org/anthology/D12-1054

  • Mohammad, S. (2018). Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 174–184

  • Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). Semeval-2018 task 1: Affect in tweets. In: Proceedings of the 12th international workshop on semantic evaluation, pp. 1–17

  • Page, K.R., Lewis, D., & Weigl, D.M. (2019). Meld: A linked data framework for multimedia access to music digital libraries. In: 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 434–435. IEEE

  • Parisi, L., Francia, S., Olivastri, S., & Tavella, M.S. (2019). Exploiting synchronized lyrics and vocal features for music emotion detection. CoRR arXiv:1901.04831

  • Pauwels, J., O’Hanlon, K., Fazekas, G., & Sandler, M. (2017). Confidence measures and their applications in music labelling systems based on hidden Markov models. In: Proceedings 18th Int. Soc. Music Information Retrieval (ISMIR 2017)

  • Pauwels, J., & Sandler, M. (2019). A web-based system for suggesting new practice material to music learners based on chord content. In: Joint Proceedings 24th ACM IUI Workshops (IUI2019)

  • Pauwels, J., Xambó, A., Roma, G., Barthet, M., & Fazekas, G. (2018). Exploring real-time visualisations to support chord learning with a large music collection. In: Proceedings 4th Web Audio Conference (WAC 2018)

  • Raimond, Y., Abdallah, S., Sandler, M., & Giasson, F. (2007). The Music Ontology. In: Proceedings of the 8th ISMIR Conference, pp. 417–422

  • Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.

    Article  Google Scholar 

  • Sterckx, L. (2014). Topic detection in a million songs. Ph.D. thesis, PhD thesis, Ghent University

  • Stöter, F.R., Uhlich, S., Liutkus, A., & Mitsufuji, Y. (2019). Open-unmix-a reference implementation for music source separation. Journal of Open Source Software

  • Tagg, P. (1982). Analysing popular music: Theory, method and practice. Popular Music, 2, 37–67. https://doi.org/10.1017/S0261143000001227

    Article  Google Scholar 

  • Vanni, L., Ducoffe, M., Aguilar, C., Precioso, F., & Mayaffre, D. (2018). Textual deconvolution saliency (tds): A deep tool box for linguistic analysis. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 548–557

  • Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 english lemmas. Behavior research methods, 45(4), 1191–1207.

    Article  Google Scholar 

  • Watanabe, K., Matsubayashi, Y., Orita, N., Okazaki, N., Inui, K., Fukayama, S., Nakano, T., Smith, J., & Goto, M. (2016). Modeling discourse segments in lyrics using repeated patterns. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1959–1969

  • Xia, Y., Wang, L., Wong, K.F., & Xu, M. (2008). Sentiment vector space model for lyric-based song sentiment classification. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, HLT-Short ’08, pp. 133–136. Association for Computational Linguistics, Stroudsburg, PA, USA. http://dl.acm.org/citation.cfm?id=1557690.1557725

  • Yang, D., & Lee, W. (2009). Music emotion identification from lyrics. In: 2009 11th IEEE International Symposium on Multimedia, pp. 624–629. https://doi.org/10.1109/ISM.2009.123

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Acknowledgements

This work is partly funded by the French Research National Agency (ANR) under the WASABI project (contract ANR-16-CE23-0017-01).

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Fell, M., Cabrio, E., Tikat, M. et al. The WASABI song corpus and knowledge graph for music lyrics analysis. Lang Resources & Evaluation (2022). https://doi.org/10.1007/s10579-022-09601-8

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  • DOI: https://doi.org/10.1007/s10579-022-09601-8

Keywords

  • Corpus (creation, annotation, etc.)
  • Information extraction
  • Information retrieval
  • Knowledge graph
  • Music and song lyrics