Have a personal or library account? Click to login
ChoraleBricks: A Modular Multitrack Dataset for Wind Music Research Cover

ChoraleBricks: A Modular Multitrack Dataset for Wind Music Research

Open Access
|Apr 2025

References

  1. Albrecht, C. (2017). Der Tonmeister. (2nd ed.). Schiele & Schön.
  2. Bandiera, G., Picas, O. R., Tokuda, H., Hariya, W., Oishi, K., and Serra, X. (2016). Good‑sounds. org: A framework to explore goodness in instrumental sounds. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), New York City, New York, USA.
  3. Battisti, F. L. (2018). The new winds of change: The evolution of the contemporary American wind band/ensemble and its music. The New Winds of Change, Hal Leonard Corporation. 978‑1‑57463‑474‑7.
  4. Berndt, A. (2022). The music performance markup format and ecosystem. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Bengaluru, India. 10.5281/zenodo.5624429
  5. Bittner, R. M., Salamon, J., Tierney, M., Mauch, M., Cannam, C., and Bello, J. P. (2014). MedleyDB: A multitrack dataset for annotation‑intensive MIR research. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan (pp. 155160).
  6. Camacho, A., and Harris, J. G. (2008). A sawtooth waveform inspired pitch estimator for speech and music. Journal of the Acoustical Society of America, 124(3), 16381652.
  7. Cannam, C., Landone, C., and Sandler, M. B. (2010). Sonic Visualiser: An open source application for viewing, analysing, and annotating music audio files. In Proceedings of the International Conference on Multimedia, Florence, Italy (pp. 14671468).
  8. Cuthbert, M. S., and Ariza, C. (2010). Music21: A toolkit for computer‑aided musicology and symbolic music data. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Utrecht, The Netherlands (pp. 637642).
  9. Dannenberg, R. B., and Raphael, C. (2006). Music score alignment and computer accompaniment. Communications of the ACM, Special Issue: Music Information Retrieval, 49(8), 3843.
  10. de Cheveigné, A., and Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. Journal of the Acoustical Society of America (JASA), 111(4), 19171930.
  11. Duan, Z., and Pardo, B. (2011). Soundprism: An online system for score‑informed source separation of music audio. IEEE Journal of Selected Topics in Signal Processing, 5(6), 12051215.
  12. EBU. (2023). R 128—loudness normalisation and permitted maximum level of audio signals. Technical report. European Broadcasting Union. https://tech.ebu.ch/docs/r/ r128.pdf
  13. Engel, J., Hantrakul, L., Gu, C., and Roberts, A. (2020). DDSP: Differentiable digital signal processing. In Proceedings of the International Conference on Learning Representations (ICLR), Virtual. https://openreview.net/forum?id=B1x1ma4tDr
  14. Fabbro, G., Uhlich, S., Lai, C.‑H., Choi, W., Martínez‑Ramírez, M., Liao, W., Gadelha, I., Ramos, G., Hsu, E., Rodrigues, H., Stöter, F., Défossez, A., Luo, Y., Yu, J., Chakraborty, D., Mohanty, S., Solovyev, R., Stempkovskiy, A., Habruseva, T., . . . Mitsufuji, Y. (2024). The sound demixing challenge 2023—music demixing track. Transactions of the International Society for Music Information Retrieval (TISMIR), 7(1), 6384. 10.5334/tismir.171
  15. Gerhardt, K., and Kirsch, M. (2024). Choralbücher from Northern Germany c. 1800—a Dataset for Studies in Hymnology, Music Culture and Figured Bass. Transactions of the International Society for Music Information Retrieval, 7(1), 25143298. https://transactions.ismir.net/articles/10.5334/tismir.191 10.5334/tismir.191
  16. Hankinson, A., Roland, P., and Fujinaga, I. (2011, October). The music encoding initiative as a document‑encoding framework. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida, USA (pp. 293298).
  17. Hawthorne, C., Simon, I., Roberts, A., Zeghidour, N., Gardner, J., Manilow, E., and Engel, J. H. (2022). Multi‑instrument music synthesis with spectrogram diffusion. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 598607). https://archives.ismir.net/ismir2022/paper/000072.pdf
  18. Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C. A., Dieleman, S., Elsen, E., Engel, J. H., and Eck, D. (2019). Enabling factorized piano music modeling and generation with the MAESTRO dataset. In Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, Louisiana, USA. https://openreview.net/forum?id=r1lYRjC9F7
  19. Hofer, A. (1992). Blasmusikforschung: Eine kritische Einführung (1st ed.). Wissenschaftliche Buchgesellschaft Darmstadt. 3‑534‑11083‑8
  20. Howard, D. M. (2007). Intonation drift in a capella soprano, alto, tenor, bass quartet singing with key modulation. Journal of Voice, 21(3), 300315.
  21. Kaiser, U., Mestemacher, I., and Vieregg, M. (2023). Operation Beethoven. Ein Projekt der Open Music Academy in Kooperation mit der Hofkapelle München. https://openmusic.academy/revs/JwXCJwcEyAXsJDNsU7DtN3
  22. Kepper, J., and Roland, P. D. (2023). The music encoding initiative guidelines version 5.0. Music Encoding Initiative. 10.5281/ZENODO.10040258
  23. Kim, J. W., Salamon, J., Li, P., and Bello, J. P. (2018). CREPE: A convolutional representation for pitch estimation. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada (pp. 161165). 10.1109/ICASSP.2018.8461329
  24. Li, B., Liu, X., Dinesh, K., Duan, Z., and Sharma, G. (2019). Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications. IEEE Transactions on Multimedia, 21(2), 522535.
  25. Maman, B., and Bermano, A. H. (2022). Unaligned supervision for automatic music transcription in the wild. In Proceedings of the International Conference on Machine Learning (ICML), Baltimore, Maryland, USA (pp. 1491814934).
  26. Maman, B., Zeitler, J., Müller, M., and Bermano, A. H. (2024). Performance conditioning for diffusion‑based multi‑instrument music synthesis. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Seoul, South Korea (pp. 50455049).
  27. Mauch, M., and Dixon, S. (2014). pYIN: A fundamental frequency estimator using probabilistic threshold distributions. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy (pp. 659663).
  28. Mauersberger, R., and Mauersberger, E. (1955). Neues Thüringer Choralbuch (1st ed.). Evangelische Verlagsanstalt Berlin.
  29. McFee, B., Kim, J. W., Cartwright, M., Salamon, J., Bittner, R. M., and Bello, J. P. (2019). Open‑source practices for music signal processing research: Recommendations for transparent, sustainable, and reproducible audio research. IEEE Signal Processing Magazine, 36(1), 128137.
  30. Müller, M. (2015). Fundamentals of Music Processing—Audio, Analysis, Algorithms, Applications (pp. 1480). Springer Verlag. 10.1007/978-3-319-21945-5 978‑3‑319‑21944‑8
  31. 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), 5262. 10.1109/MSP.2018.2868887, https://ieeexplore.ieee.org/document/8588416/
  32. Müller, M., and Zalkow, F. (2021). libfmp: A Python package for fundamentals of music processing. Journal of Open Source Software (JOSS), 6(63), 15, 3326. 10.21105/joss.03326, 2021_MuellerZalkow_libfmp_JOSS.pdf, https://github.com/meinardmueller/libfmp
  33. Muse Group. (2022, December). MuseScore v4.0. https://musescore.org
  34. Niemann, N. (2006). Bläserklang im Gottes‑Dienst—Ein Streifzug durch 3000 Jahre Gotteslob. Verein zur Forderung der Posaunenchorarbeit in derEvangelisch‑lutherischen Landeskirche in Braunschweig e.V., Germany.
  35. Özer, Y., Brütting, L., Schwär, S., and Müller, M. (2024). libsoni: A Python toolbox for sonifying music annotations and feature representations. Journal of Open Source Software (JOSS), 9(96), 16. 10.21105/joss.06524
  36. Peeters, G., and Richard, G. (2021). Deep Learning for Audio and Music, Multi‑faceted Deep Learning: Models and Data (pp. 231266). Springer International Publishing. 10.1007/978-3-030-74478-6_10
  37. Pereira, I., Araújo, F., Korzeniowski, F., and Vogl, R. (2023). MoisesDB: A dataset for source separation beyond 4‑stems. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Milan, Italy (pp. 619626).
  38. Raffel, C., McFee, B., Humphrey, E. J., Salamon, J., Nieto, O., Liang, D., and Ellis, D. P. W. (2014). MIR_EVAL: A transparent implementation of common MIR metrics. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan (pp. 367372).
  39. Rafii, Z., Liutkus, A., Stöter, F., Mimilakis, S. I., and Bittner, R. (2017). The MUSDB18 corpus for music separation. 10.5281/zenodo.1117372
  40. Richard, G., Lostanlen, V., Yang, Y.‑H., and Müller, M. (2024). Model‑based deep learning for music information research: Leveraging diverse knowledge sources to enhance explainability, controllability, and resource efficiency. IEEE Signal Processing Magazine, 41(6), 5159.
  41. Richard, G., Smaragdis, P., Gannot, S., Naylor, P. A., Makino, S., Kellermann, W., and Sugiyama, A. (2023). Audio signal processing in the 21st century: The important outcomes of the past 25 years. IEEE Signal Processing Magazine, 40(5), 1226. 10.1109/MSP.2024.3415569, https://ieeexplore.ieee.org/document/10819669
  42. Roa Dabike, G., Cox, T. J., Miller, A. J., Fazenda, B. M., Graetzer, S., Vos, R. R., Akeroyd, M. A., Firth, J., Whitmer, W. M., Bannister, S., Greasley, A., and Barker, J. P. (2024). The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learning. Data in Brief, 57, 23523409. 10.1016/j.dib.2024.111199, https://www.sciencedirect.com/science/article/pii/S2352340924011612
  43. Salamon, J., Bittner, R. M., Bonad., J., Vicente, J. J. B., Gómez, E., and Bello, J. P. (2017). An analysis/synthesis framework for automatic f0 annotation of multitrack datasets. In Proceedings of International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China (pp. 7178). 10.5281/zenodo.1415588, MDB‑melodysynth
  44. Sarkar, S., Benetos, E., and Sandler, M. (2022). EnsembleSet: A new high quality dataset for chamber ensemble separation. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Bengaluru, India (pp. 625632).
  45. Schwär, S., Rosenzweig, S., and Müller, M. (2021). A differentiable cost measure for intonation processing in polyphonic music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 626633).
  46. Watcharasupat, K. N., and Lerch, A. (2024). A stem‑agnostic single‑decoder system for music source separation beyond four stems. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), San Francisco, CA, USA.
  47. Weiß, C., Arifi‑Müller, V., Prätzlich, T., Kleinertz, R., and Müller, M. (2016). Analyzing measure annotations for Western classical music recordings. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), New York, USA (pp. 517523). 10.5281/zenodo.1417449
  48. Werner, N., Balke, S., Stöter, F.‑R., Müller, M., and Edler, B. (2017). trackswitch.js: A versatile web‑based audio player for presenting scientific results. In Proceedings of the Web Audio Conference (WAC), London, UK. 2017 WernerBSME TrackswitchJSPlayer WAC.pdf
  49. Wu, Y., Gardner, J., Manilow, E., Simon, I., Hawthorne, C., and Engel, J. (2022). The Chamber ensemble generator: Limitless high‑quality MIR data via generative modeling. http://arxiv.org/abs/2209.14458, 2024‑03‑18, arXiv
DOI: https://doi.org/10.5334/tismir.252 | Journal eISSN: 2514-3298
Language: English
Submitted on: Jan 17, 2025
Accepted on: Mar 27, 2025
Published on: Apr 30, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Stefan Balke, Axel Berndt, Meinard Müller, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.