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References

  1. 1Arzt, A. (2016). Flexible and Robust Music Tracking. PhD thesis, Johannes Kepler University Linz, Linz, Austria.
  2. 2Cancino-Chacón, C., Grachten, M., Goebl, W., and Widmer, G. (2018). Computational models of expressive music performance: A comprehensive and critical review. Frontiers in Digital Humanities, 5:125. DOI: 10.3389/fdigh.2018.00025
  3. 3Cancino-Chacón, C., Peter, S. D., Karystinaios, E., Foscarin, F., Grachten, M., and Widmer, G. (2022). Partitura: A python package for symbolic music processing. In Proceedings of the Music Encoding Conference (MEC), Halifax, Canada.
  4. 4Cancino-Chacón, C. E., Gadermaier, T., Widmer, G., and Grachten, M. (2017). An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music. Machine Learning, 106(6):887909. DOI: 10.1007/s10994-017-5631-y
  5. 5Chen, C.-T., Jang, J.-S. R., and Liou, W. (2014). Improved score-performance alignment algorithms on polyphonic music. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 13651369. DOI: 10.1109/ICASSP.2014.6853820
  6. 6Dannenberg, R. B. (1984). An on-line algorithm for realtime accompaniment. In Proceedings of the 1984 International Computer Music Conference, pages 193198, Paris, France.
  7. 7Dixon, S. and Goebl, W. (2002). Pinpointing the beat: Tapping to expressive performances. In Proceedings of the 7th International Conference on Music Perception and Cognition (ICMPC7), pages 617620, Sydney, Australia.
  8. 8Emiya, V., Badeau, R., and David, B. (2010). Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle. IEEE Transactions on Audio, Speech, and Language Processing, 18(6):16431654. DOI: 10.1109/TASL.2009.2038819
  9. 9Field, A., Miles, J., and Field, Z. (2012). Discovering Statistics Using R. Sage Publishing.
  10. 10Flossmann, S., Goebl, W., Grachten, M., Niedermayer, B., and Widmer, G. (2010). The Magaloff Project: An interim report. Journal of New Music Research, 39(4):363377. DOI: 10.1080/09298215.2010.523469
  11. 11Foscarin, F., Karystinaios, E., Peter, S. D., Cancino-Chacon, C., Grachten, M., and Widmer, G. (2022). The match file format: Encoding alignments between scores and performances. In Proceedings of the Music Encoding Conference (MEC), Halifax, Canada.
  12. 12Foscarin, F., McLeod, A., Rigaux, P., Jacquemard, F., and Sakai, M. (2020). ASAP: A dataset of aligned scores and performances for piano transcription. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 534541.
  13. 13Gadermaier, T. and Widmer, G. (2019). A study of annotation and alignment accuracy for performance comparison in complex orchestral music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands.
  14. 14Gingras, B. and McAdams, S. (2011). Improved scoreperformance matching using both structural and temporal information from MIDI recordings. Journal of New Music Research, 40(1):4357. DOI: 10.1080/09298215.2010.545422
  15. 15Goebl, W. (1999). The Vienna 4x22 Piano Corpus. http://repo.mdw.ac.at/projects/IWK/the_vienna_4x22_piano_corpus/index.html.
  16. 16Goebl, W. (2001). Melody lead in piano performance: Expressive device or artifact? The Journal of the Acoustical Society of America, 110(1):563572. DOI: 10.1121/1.1376133
  17. 17Goebl, W., Dixon, S., De Poli, G., Friberg, A., Bresin, R., and Widmer, G. (2008). ‘Sense’ in expressive music performance: Data acquisition, computational studies, and models. Polotti, P. and Rocchesso, D., editors, Sound to Sense – Sense to Sound: A State of the Art in Sound and Music Computing, pages 195242. Logos, Berlin.
  18. 18Goebl, W. and Palmer, C. (2009). Synchronization of timing and motion among performing musicians. Music Perception, 26(5):427438. DOI: 10.1525/mp.2009.26.5.427
  19. 19Grachten, M., Gasser, M., Arzt, A., and Widmer, G. (2013). Automatic alignment of music performances with structural differences. In Proceedings of the 14th International Society for Music Information Retrieval Conference, Curitiba, Brazil.
  20. 20Grosche, P., Müller, M., and Sapp, C. S. (2010). What makes beat tracking difficult? A case study on Chopin mazurkas. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 649654.
  21. 21Gu, Y. and Raphael, C. (2009). Orchestral accompaniment for a reproducing piano. In Proceedings of the International Computer Music Conference (ICMC09), pages 501504, Montreal, Canada.
  22. 22Hashida, M., Matsui, T., and Katayose, H. (2008). A new music database describing deviation information of performance expressions. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 489494.
  23. 23Hashida, M., Nakamura, E., and Katayose, H. (2017). Constructing PEDB 2nd Edition: A music performance database with phrase information. In Proceedings of the 14th Sound and Music Computing Conference (SMC 2017), pages 359364, Espoo, Finland.
  24. 24Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C.-Z. A., Dieleman, S., Elsen, E., Engel, J., and Eck, D. (2019). Enabling factorized piano music modeling and generation with the MAESTRO dataset. In International Conference on Learning Representations.
  25. 25Henkel, F., Kelz, R., and Widmer, G. (2020). Learning to read and follow music in complete score sheet images. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 780787.
  26. 26Jeong, D., Kwon, T., Kim, Y., Lee, K., and Nam, J. (2019). VirtuosoNet: A hierarchical RNN-based system for modeling expressive piano performance. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 908915, Delft, The Netherlands.
  27. 27Lerch, A., Arthur, C., Pati, A., and Gururani, S. (2020). An interdisciplinary review of music performance analysis. Transactions of the International Society for Music Information Retrieval, 3(1):221245. DOI: 10.5334/tismir.53
  28. 28Levandowsky, M. and Winter, D. (1971). Distance between sets. Nature, 234(5323):3435. DOI: 10.1038/234034a0
  29. 29Müller, M. (2015). Fundamentals of Music Processing –Audio, Analysis, Algorithms, Applications. Springer. DOI: 10.1007/978-3-319-21945-5
  30. 30Müller, M., Kurth, F., and Roder, T. (2004). Towards an efficient algorithm for automatic score-to-audio synchronization. In Proceedings of the International Conference on Music Information Retrieval (ISMIR).
  31. 31Müller, 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, page 3434. DOI: 10.21105/joss.03434
  32. 32Nakamura, E., Benetos, E., Yoshii, K., and Dixon, S. (2018). Towards complete polyphonic music transcription: Integrating multi-pitch detection and rhythm quantization. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 101105. DOI: 10.1109/ICASSP.2018.8461914
  33. 33Nakamura, E., Ono, N., Sagayama, S., and Watanabe, K. (2015). A stochastic temporal model of polyphonic MIDI performance with ornaments. Journal of New Music Research, 44(4):287304. DOI: 10.1080/09298215.2015.1078819
  34. 34Nakamura, E., Yoshii, K., and Katayose, H. (2017). Performance error detection and post-processing for fast and accurate symbolic music alignment. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 347353, Suzhou, China.
  35. 35Needleman, S. B. and Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3):443453. DOI: 10.1016/0022-2836(70)90057-4
  36. 36Prätzlich, T., Driedger, J., and Müller, M. (2016). Memory-restricted multiscale dynamic time warping. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 569573. DOI: 10.1109/ICASSP.2016.7471739
  37. 37Sapp, C. S. (2007). Comparative analysis of multiple musical performances. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Vienna, Austria.
  38. 38Schwarz, D., Orio, N., and Schnell, N. (2004). Robust polyphonic MIDI score following with hidden Markov models. In Proceedings of the International Computer Music Conference (ICMC), pages 442445, Miami, FL, USA.
  39. 39Vercoe, B. (1984). The synthetic performer in the context of live performance. In Proceedings of the International Computer Music Conference, pages 199200, Paris, France.
  40. 40Wang, S. (2017). Computational Methods for the Alignment and Score-Informed Transcription of Piano Music. PhD thesis, Queen Mary University of London, London, UK.
  41. 41Weiß, 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 (JOCCH). DOI: 10.1145/3429743
  42. 42Weigl, D., Liem, C., Gómez, E., Crawford, T., Ahmed, R., Klerx, W., and Goebl, W. (2019). Towards richer online music public-domain archives: Providing enriched access to classical music encodings. In Proceedings of the Music Encoding Conference.
  43. 43Zalkow, F., Balke, S., Arifi-Müller, V., and Müller, M. (2020). MTD: A multimodal dataset of musical themes for MIR research. Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1):180192. DOI: 10.5334/tismir.68
DOI: https://doi.org/10.5334/tismir.149 | Journal eISSN: 2514-3298
Language: English
Submitted on: Sep 1, 2022
Accepted on: Jun 2, 2023
Published on: Jun 26, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Silvan David Peter, Carlos Eduardo Cancino-Chacón, Francesco Foscarin, Andrew Philip McLeod, Florian Henkel, Emmanouil Karystinaios, Gerhard Widmer, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.