References
- 1Arzt, A. (2016). Flexible and Robust Music Tracking. PhD thesis, Johannes Kepler University Linz, Linz, Austria.
- 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, 5:1–25. DOI: 10.3389/fdigh.2018.00025
- 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.
- 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):887–909. DOI: 10.1007/s10994-017-5631-y
- 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 1365–1369. DOI: 10.1109/ICASSP.2014.6853820
- 6Dannenberg, R. B. (1984). An on-line algorithm for realtime accompaniment. In Proceedings of the 1984 International Computer Music Conference, pages 193–198, Paris, France.
- 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 617–620, Sydney, Australia.
- 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):1643–1654. DOI: 10.1109/TASL.2009.2038819
- 9Field, A., Miles, J., and Field, Z. (2012). Discovering Statistics Using R. Sage Publishing.
- 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):363–377. DOI: 10.1080/09298215.2010.523469
- 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.
- 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 534–541.
- 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.
- 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):43–57. DOI: 10.1080/09298215.2010.545422
- 15Goebl, W. (1999). The Vienna 4x22 Piano Corpus.
http://repo.mdw.ac.at/projects/IWK/the_vienna_4x22_piano_corpus/index.html . - 16Goebl, W. (2001). Melody lead in piano performance: Expressive device or artifact? The Journal of the Acoustical Society of America, 110(1):563–572. DOI: 10.1121/1.1376133
- 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 195–242. Logos, Berlin. - 18Goebl, W. and Palmer, C. (2009). Synchronization of timing and motion among performing musicians. Music Perception, 26(5):427–438. DOI: 10.1525/mp.2009.26.5.427
- 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.
- 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 649–654.
- 21Gu, Y. and Raphael, C. (2009). Orchestral accompaniment for a reproducing piano. In Proceedings of the International Computer Music Conference (ICMC09), pages 501–504, Montreal, Canada.
- 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 489–494.
- 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 359–364, Espoo, Finland.
- 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.
- 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 780–787.
- 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 908–915, Delft, The Netherlands.
- 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):221–245. DOI: 10.5334/tismir.53
- 28Levandowsky, M. and Winter, D. (1971). Distance between sets. Nature, 234(5323):34–35. DOI: 10.1038/234034a0
- 29Müller, M. (2015). Fundamentals of Music Processing –Audio, Analysis, Algorithms, Applications. Springer. DOI: 10.1007/978-3-319-21945-5
- 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).
- 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
- 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 101–105. DOI: 10.1109/ICASSP.2018.8461914
- 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):287–304. DOI: 10.1080/09298215.2015.1078819
- 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 347–353, Suzhou, China.
- 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):443–453. DOI: 10.1016/0022-2836(70)90057-4
- 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 569–573. DOI: 10.1109/ICASSP.2016.7471739
- 37Sapp, C. S. (2007). Comparative analysis of multiple musical performances. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Vienna, Austria.
- 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 442–445, Miami, FL, USA.
- 39Vercoe, B. (1984). The synthetic performer in the context of live performance. In Proceedings of the International Computer Music Conference, pages 199–200, Paris, France.
- 40Wang, S. (2017). Computational Methods for the Alignment and Score-Informed Transcription of Piano Music. PhD thesis, Queen Mary University of London, London, UK.
- 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
- 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.
- 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):180–192. DOI: 10.5334/tismir.68
