References
- Allegraud, P., Bigo, L., Feisthauer, L., Giraud, M., Groult, R., Leguy, E., and Levé, F. (2019). Learning sonata form structure on Mozart’s string quartets. Transactions of the International Society for Music Information Retrieval, 2(1): 82–96.
- Balke, S., Dittmar, C., Abeßer, J., Frieler, K., Pfleiderer, M., and Müller, M. (2018). Bridging the gap: Enriching YouTube videos with jazz music annotations. Frontiers in Digital Humanities, 5: 1.
- Berndt, A. (2021). The music performance markup format and ecosystem. In International Society for Music Information Retrieval Conference (ISMIR 2021) (pp. 50–57).
- Borsan, V. N., Giraud, M., Groult, R., and Lecroq, T. (2023). Adding context to content improves pattern matching: A study on Slovenian folksongs. In International Society for Music Information Retrieval Conference (ISMIR 2023) (pp. 474–481).
- Borsan, V. N., Kovačič, M., Giraud, M., Pisk, M., Pesek, M., and Marolt, M. (2025). Introducing the digitised dataset of Slovenian Folk Ballads. Ethnomusicology Forum, in press.
- Cancino‑Chacón, C., Peter, S. D., Karystinaios, E., Foscarin, F., Grachten, M., and Widmer, G. (2023). Partitura: A python package for symbolic music processing. In Music Encoding Conference (MEC 2022).
- Couturier, L., Bigo, L., and Levé, F. (2022). A dataset of texture annotations in Mozart piano sonatas. In International Society for Music Information Retrieval Conference (ISMIR 2022).
- Cuthbert, M. S., and Ariza, C. (2010). music21: A toolkit for computer‑aided musicology and symbolic music data. In International Society for Music Information Retrieval Conference (ISMIR 2010) (pp. 637–642).
- de Berardinis, J., Carriero, V. A., Meroño‑Peñuela, A., Poltronieri, A., and Presutti, V. (2022). The music meta ontology: A flexible semantic model for the interoperability of music metadata. In International Society for Music Information Retrieval Conference (ISMIR 2023) (pp. 859–867).
- de Berardinis, J., Meroño‑Peñuela, A., Poltronieri, A., and Presutti, V. (2023). Choco: A chord corpus and a data transformation workflow for musical harmony knowledge graphs. Scientific Data, 10(1): 641.
- Devaney, J. (2020). Using note‑level music encodings to facilitate interdisciplinary research on human engagement with music. Transactions of the International Society for Music Information Retrieval, 3(1).
- Dorfer, M., Henkel, F., and Widmer, G. (2018). Learning to listen, read, and follow: Score following as a reinforcement learning game. In International Society for Music Information Retrieval Conference (ISMIR 2018).
- Ewert, S., Müller, M., and Grosche, P. (2009). High resolution audio synchronization using chroma onset features. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009) (pp. 1869–1872).
- Fremerey, C., Müller, M., and Clausen, M. (2010). Handling repeats and jumps in score‑performance synchronization. In International Society for Music Information Retrieval Conference (ISMIR 2010) (pp. 243–248).
- Fyfe, L., Bedoya, D., and Chew, E. (2022). Annotation and analysis of recorded piano performances on the web. Journal of the Audio Engineering Society, 70(11).
- Garczynski, L., Giraud, M., Leguy, E., and Rigaux, P. (2022). Modeling and editing cross‑modal synchronization on a label web canvas. In Music Encoding Conference (MEC 2022) (pp. 61–71).
- Giraud, M., Groult, R., Leguy, E., and Levé, F. (2015). Computational fugue analysis. Computer Music Journal, 39(2).
- Gotham, M., Hentschel, J., Couturier, L., Dykeaylen, N., Rohrmeier, M., and Giraud, M. (2023a). The ‘Measure Map’: An inter‑operable standard for aligning symbolic music. In Digital Libraries for Musicology (DLfM 2023) (pp. 91–99).
- Gotham, M., and Ireland, M. (2019). Taking form: A representation standard, conversion code, and example corpus for recording, visualizing, and studying analyses of musical form. In International Society for Music Information Retrieval Conference (ISMIR 2019) (pp. 693–699).
- Gotham, M., Micchi, G., López, N. N., and Sailor, M. (2023b). When in Rome: A meta‑corpus of functional harmony. Transactions of the International Society for Music Information Retrieval, 6(1): 150–166.
- Gotham, M. R. H., and Jonas, P. (2021). The OpenScore Lieder corpus. In Music Encoding Conference (MEC 2021) (pp. 131–136).
- Guillotel‑Nothmann, C., Rigaux, P., Coüasnon, B. B., Giraud, M., and Lemaitre, A. (2024). The Collab‑Score project – from optical recognition to multimodal music sources. In Workshop on Reading Music Systems (WoRMS 2024) (pp. 33–37).
- Hentschel, J., Neuwirth, M., and Rohrmeier, M. (2021). The annotated Mozart sonatas: Score, harmony, and cadence. Transactions of the International Society for Music Information Retrieval, 4(1): 67–80.
- Le, D.‑V.‑T., Bigo, L., Herremans, D., and Keller, M. (2025). Natural language processing methods for symbolic music generation and information retrieval: A survey. ACM Computing Surveys.
- Le, D.‑V.‑T., Giraud, M., Levé, F., and Maccarini, F. (2022). A corpus describing orchestral texture in first movements of classical and early‑romantic symphonies. In Digital Libraries for Musicology (DLfM 2022) (pp. 22–35).
- Lewis, D., Shibata, E., Saccomano, M., Rosendahl, L., Kepper, J., Hankinson, A., Siegert, C., and Page, K. (2022). A model for annotating musical versions and arrangements across multiple documents and media. In Digital Libraries for Musicology (DLfM 2022) (pp. 10–18).
- Mazzoni, D., and Dannenberg, R. B. (2002). A fast data structure for disk‑based audio editing. Computer Music Journal, 26(2): 62–76.
- McFee, B., Kim, J. W., Cartwright, M., Salamon, J., Bittner, R. M., and Bello, J. P. (2019). Opensource practices for music signal processing research: Recommendations for transparent, sustainable, and reproducible audio research. IEEE Signal Processing Magazine, 36(1): 128–137.
- McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Python in Science Conference (SciPy 2015) (pp. 18–24).
- Moss, F. C., and Neuwirth, M. (2021). FAIR, open, linked: Introducing the special issue on open science in musicology. Empirical Musicology Review, 16(1): 1–4.
- Müller, M., Özer, Y., Krause, M., Prätzlich, T., and Driedger, J. (2021). Sync Toolbox: A Python package for efficient, robust, and accurate music synchronization. Journal of Open Source Software (JOSS), 6(64): 3434:1–4.
- Müller, M., Arzt, A., Balke, S., Dorfer, M., and Widmer, G. (2019). Cross‑modal music retrieval and applications: An overview of key methodologies. IEEE Signal Processing Magazine, 36(1): 52–62.
- Nattiez, J.‑J. (1975). Fondements d’une sémiologie de la musique. Dufrenne.
- Panteli, M., Benetos, E., and Dixon, S. (2018). A review of manual and computational approaches for the study of world music corpora. Journal of New Music Research, 47(2): 176–189.
- Peeters, G., and Fort, K. (2012). Towards a (better) definition of annotated MIR corpora. In International Society for Music Information Retrieval Conference (ISMIR 2012) (pp. 25–30).
- Pfleiderer, M., Frieler, K., Abeßer, J., Zaddach, W.‑G., and Burkhart, B. (Eds.). (2017). Inside the Jazzomat – New Perspectives for Jazz Research. Schott Campus.
- Poltronieri, A., and Gangemi, A. (2022). The HaMSE ontology: Using semantic technologies to support music representation interoperability and musicological analysis. In Multisensory Data and Knowledge (MDK 2021) (pp. 62–76).
- Porter, A., Sordo, M., and Serra, X. (2013). Dunya: A system for browsing audio music collections exploiting cultural context. In International Society for Music Information Retrieval Conference (ISMIR 2013) (pp. 101–106).
- Pugin, L., Zitellini, R., and Roland, P. (2014). Verovio: A library for engraving MEI music notation into SVG. In International Society for Music Information Retrieval Conference (ISMIR 2014) (pp. 107–112).
- Rosenzweig, S., Scherbaum, F., Shugliashvili, D., Arifi‑Müller, V., and Müller, M. (2020). Erkomaishvili dataset: A curated corpus of traditional georgian vocal music for computational musicology. Transactions of the International Society for Music Information Retrieval, 3(1): 31–41.
- Sauda, A., Giraud, M., and Leguy, E. (2022). Soutenir en classe l’écoute active, l’autonomie et l’échange en analyse musicale avec la plateforme web Dezrann. In Sound and Music Computing / Journées d’Informatique Musicale (SMC 2022 / JIM 2022) (pp. 617–623).
- Savage, P. E. (2022).
An overview of cross‑cultural music corpus studies . In The Oxford Handbook of Music and Corpus Studies. Oxford University Press. - Schöning, K., de Valk, R., Weigl, D. M., Kyriazis, I., Janjuš, O., Burghoff, H., and Steindl, C. (2025). A collaborative digital edition of 15th‑ and 16th‑century german lute tablature: The E‑LAUTE project. Journal of New Music Research, 1–16.
- Serra, X. (2014). Creating research corpora for the computational study of music: The case of the CompMusic project. In AES International Conference on Semantic Audio.
- Shi, Z., Sapp, C., Arul, K., McBride, J., and Smith III, J. O. (2019). SUPRA: Digitizing the stanford university piano roll archive. In International Society for Music Information Retrieval Conference (ISMIR 2019) (pp. 517–523).
- Shugliashvili, D. (2014). Georgian Church Hymns (Shemokmedi School): Transcriptions of Artem Erkomaishvili Recordings (2nd ed.).
- Shuker, R. (2013). Understanding Popular Music Culture. Routledge.
- Solie, R. A. (Ed.). (1993). Musicology and Difference: Gender and Sexuality in Music Scholarship. University of California Press.
- Thickstun, J., Brennan, J., and Verma, H. (2020). Rethinking evaluation methodology for audio‑toscore alignment. arXiv preprint arXiv:2009.14374.
- Thomas, V., Fremerey, C., Müller, M., and Clausen, M. (2012). Linking sheet music and audio – challenges and new approaches. In Multimodal Music Processing (Vol. 3, pp. 1–22).
- Wang, L., Zhao, Z., Liu, H., Pang, J., Qin, Y., and Wu, Q. (2024). A review of intelligent music generation systems. Neural Computing and Applications, 36(12): 6381–6401.
- Weigl, D. M., Crawford, T., Gkiokas, A., Goebl, W., Gómez, E., Guti, N., Liem, C. C. S., and Santos, P. (2021a). FAIR interconnection and enrichment of public‑domain music resources on the web. Empirical Musicology Review, 16(1): 16–33.
- Weigl, D. M., Goebl, W., Baker, D. J., Crawford, T., Zubani, F., Gkiokas, A., Gutierrez, N. F., Porter, A., and Santos, P. (2021b). Notes on the music: A social data infrastructure for music annotation. In Digital Libraries for Musicology (DLfM 2021) (pp. 23–31).
- Weigl, D. M., VanderHart, C., Rammler, D., Pescoller, M., and Goebl, W. (2023). Listen here! a web‑native digital musicology environment for machine‑assisted close listening. In Digital Libraries for Musicology (DLfM 2023) (pp. 109–118).
- Weiß, C., Zalkow, F., Arifi‑Müller, V., Müller, M., Koops, H. V., Volk, A., and Grohganz, H. G. (2021). Schubert Winterreise Dataset: A multimodal scenario for music analysis. Journal on Computing and Cultural Heritage, 14(2).
- Werner, N., Balke, S., Stöter, F.‑R., Müller, M., and Edler, B. (2017). trackswitch.js: A versatile webbased audio player for presenting scientific results. In Web Audio Conference (WAC 2017).
- Winget, M. A. (2008). Annotations on musical scores by performing musicians. Journal of the Association for Information Science and Technology, 59(12): 1878–1897.
- Zalkow, F., Rosenzweig, S., Graulich, J., Dietz, L., Lemnaouar, E. M., and Müller, M. (2018). A web‑based interface for score following and track switching in choral music. In International Society for Music Information Retrieval Conference (ISMIR 2018). late‑breaking demo.