{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:03:31Z","timestamp":1765544611231,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T00:00:00Z","timestamp":1489017600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004963","name":"Seventh Framework Programme","doi-asserted-by":"publisher","award":["610859","project Lrn2Cre8"],"award-info":[{"award-number":["610859","project Lrn2Cre8"]}],"id":[{"id":"10.13039\/501100004963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["670035","project CON ESPRESSIONE"],"award-info":[{"award-number":["670035","project CON ESPRESSIONE"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2017,6]]},"DOI":"10.1007\/s10994-017-5631-y","type":"journal-article","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T20:34:54Z","timestamp":1489091694000},"page":"887-909","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music"],"prefix":"10.1007","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5770-7005","authenticated-orcid":false,"given":"Carlos Eduardo","family":"Cancino-Chac\u00f3n","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thassilo","family":"Gadermaier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerhard","family":"Widmer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maarten","family":"Grachten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,9]]},"reference":[{"key":"5631_CR1","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer Science."},{"issue":"1","key":"5631_CR2","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1525\/mp.2014.32.1.51","volume":"32","author":"L Bishop","year":"2014","unstructured":"Bishop, L., Bailes, F., & Dean, R. T. (2014). Performing musical dynamics. Music Perception, 32(1), 51\u201366.","journal-title":"Music Perception"},{"issue":"3","key":"5631_CR3","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1080\/09298219808570748","volume":"27","author":"R Bresin","year":"1998","unstructured":"Bresin, R. (1998). Artificial neural networks based models for automatic performance of musical scores. Journal of New Music Research, 27(3), 239\u2013270.","journal-title":"Journal of New Music Research"},{"key":"5631_CR4","unstructured":"Cancino Chac\u00f3n, C. E., Grachten, M., & Widmer, G. (2014). Bayesian linear basis models with gaussian priors for musical expression. Technical report. Austrian Research Institute for Artificial Intelligence, Vienna, TR-2014-12."},{"key":"5631_CR5","volume-title":"Generative processes in music: The psychology of performance, improvisation, and composition","author":"EF Clarke","year":"1988","unstructured":"Clarke, E. F. (1988). Generative principles in music. In J. Sloboda (Ed.), Generative processes in music: The psychology of performance, improvisation, and composition. Oxford: Oxford University Press."},{"key":"5631_CR6","unstructured":"Dauphin, Y. N., de\u00a0Vries, H., Chung, J., & Bengio, Y. (2015). RMSProp and equilibrated adaptive learning rates for non-convex optimization. arXiv:1502.4390 ."},{"issue":"4","key":"5631_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2665078","volume":"11","author":"G Poli De","year":"2015","unstructured":"De Poli, G., Canazza, S., Rod\u00e0, A., & Schubert, E. (2015). The role of individual difference in judging expressiveness of computer-assisted music performances by experts. ACM Transactions on Applied Perception, 11(4), 1\u201320.","journal-title":"ACM Transactions on Applied Perception"},{"key":"5631_CR8","unstructured":"De Poli, G., Canazza, S., Rod\u00e0, A., Vidolin, A., & Zanon, P. (2001). Analysis and modeling of expressive intentions in music performance. In Proceedings of the international workshop on human supervision and control in engineering and music. Kassel, Germany."},{"key":"5631_CR9","unstructured":"EBU-R-128. (2011). BU Tech 3341-2011, Practical Guidelines for Production and Implementation in Accordance with EBU R 128. https:\/\/tech.ebu.ch\/docs\/tech\/tech3341.pdf ."},{"issue":"4","key":"5631_CR10","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1080\/09298215.2010.523469","volume":"39","author":"S Flossmann","year":"2010","unstructured":"Flossmann, S., Goebl, W., Grachten, M., Niedermayer, B., & Widmer, G. (2010). The Magaloff project: An interim report. Journal of New Music Research, 39(4), 363\u2013377.","journal-title":"Journal of New Music Research"},{"issue":"5","key":"5631_CR11","doi-asserted-by":"publisher","first-page":"2950","DOI":"10.1137\/10079687X","volume":"33","author":"DCL Fong","year":"2011","unstructured":"Fong, D. C. L., & Saunders, M. (2011). LSMR: An iterative algorithm for sparse least-squares problems. SIAM Journal on Scientific Computing, 33(5), 2950\u20132971.","journal-title":"SIAM Journal on Scientific Computing"},{"issue":"2\u20133","key":"5631_CR12","doi-asserted-by":"publisher","first-page":"145","DOI":"10.2478\/v10053-008-0052-x","volume":"2","author":"A Friberg","year":"2006","unstructured":"Friberg, A., Bresin, R., & Sundberg, J. (2006). Overview of the KTH rule system for musical performance. Advances in Cognitive Psychology, 2(2\u20133), 145\u2013161.","journal-title":"Advances in Cognitive Psychology"},{"issue":"3","key":"5631_CR13","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1121\/1.426687","volume":"105","author":"A Friberg","year":"1999","unstructured":"Friberg, A., & Sundberg, J. (1999). Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping runners. Journal of the Acoustical Society of America, 105(3), 1469\u20131484.","journal-title":"Journal of the Acoustical Society of America"},{"issue":"3","key":"5631_CR14","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1177\/03057356030313002","volume":"31","author":"A Gabrielsson","year":"2003","unstructured":"Gabrielsson, A. (2003). Music performance research at the millennium. The Psychology of Music, 31(3), 221\u2013272.","journal-title":"The Psychology of Music"},{"key":"5631_CR15","unstructured":"Gadermaier, T., Grachten, M., & Cancino\u00a0Chac\u00f3n, C. E. (2016). Modeling loudness variations in ensemble performance. In Proceedings of the 2nd international conference on new music concepts (ICNMC 2016). ABEditore, Treviso, Italy."},{"issue":"1","key":"5631_CR16","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1121\/1.1376133","volume":"110","author":"W Goebl","year":"2001","unstructured":"Goebl, W. (2001). Melody lead in piano performance: Expressive device or artifact? Journal of the Acoustical Society of America, 110(1), 563\u2013572.","journal-title":"Journal of the Acoustical Society of America"},{"key":"5631_CR17","doi-asserted-by":"crossref","unstructured":"Grachten, M., & Cancino Chac\u00f3n, C. E. (2017). Temporal dependencies in the expressive timing of classical piano performances. In M. Lesaffre, M. Leman, & P. J. Maes (Eds.), The Routledge companion of embodied music interaction (pp. 362\u2013371).","DOI":"10.4324\/9781315621364-40"},{"issue":"1","key":"5631_CR18","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MMUL.2017.4","volume":"24","author":"M Grachten","year":"2017","unstructured":"Grachten, M., Cancino Chac\u00f3n, C. E., Gadermaier, T., & Widmer, G. (2017). Towards computer-assisted understanding of dynamics in symphonic music. IEEE Multimedia, 24(1), 36\u201346.","journal-title":"IEEE Multimedia"},{"key":"5631_CR19","unstructured":"Grachten, M., Cancino\u00a0Chac\u00f3n, C. E., & Widmer, G. (2014). Analysis and prediction of expressive dynamics using Bayesian linear models. In Proceedings of the 1st international workshop on computer and robotic systems for automatic music performance (pp. 545\u2013552)."},{"key":"5631_CR20","unstructured":"Grachten, M., Gasser, M., Arzt, A., & 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."},{"issue":"5","key":"5631_CR21","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/TMM.2014.2311013","volume":"16","author":"M Grachten","year":"2014","unstructured":"Grachten, M., & Krebs, F. (2014). An assessment of learned score features for modeling expressive dynamics in music. IEEE Transactions on Multimedia, 16(5), 1211\u20131218. doi: 10.1109\/TMM.2014.2311013 .","journal-title":"IEEE Transactions on Multimedia"},{"issue":"4","key":"5631_CR22","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1080\/09298215.2012.731071","volume":"41","author":"M Grachten","year":"2012","unstructured":"Grachten, M., & Widmer, G. (2012). Linear basis models for prediction and analysis of musical expression. Journal of New Music Research, 41(4), 311\u2013322.","journal-title":"Journal of New Music Research"},{"key":"5631_CR23","unstructured":"Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv:1308.0850 ."},{"issue":"2\u20133","key":"5631_CR24","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10994-006-8751-3","volume":"65","author":"G Grindlay","year":"2006","unstructured":"Grindlay, G., & Helmbold, D. (2006). Modeling, analyzing, and synthesizing expressive piano performance with graphical models. Machine Learning, 65(2\u20133), 361\u2013387.","journal-title":"Machine Learning"},{"key":"5631_CR25","unstructured":"Hashida, M., Nakra, T., Katayose, H., Murao, T., Hirata, K., Suzuki, K., & Kitahara, T. (2008). Rencon: Performance rendering contest for automated music systems. In Proceedings of the 10th international conference on music perception and cognition (ICMPC), Sapporo, Japan."},{"issue":"8","key":"5631_CR26","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"issue":"5","key":"5631_CR27","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1525\/mp.2006.23.5.365","volume":"23","author":"H Honing","year":"2006","unstructured":"Honing, H. (2006). Computational modeling of music cognition: A case study on model selection. Music Perception, 23(5), 365\u2013376.","journal-title":"Music Perception"},{"key":"5631_CR28","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/oso\/9780192631886.003.0014","volume-title":"Music and emotion: Theory and research","author":"P Juslin","year":"2001","unstructured":"Juslin, P. (2001). Communicating emotion in music performance: A review and a theoretical framework. In P. Juslin & J. Sloboda (Eds.), Music and emotion: Theory and research (pp. 309\u2013337). New York: Oxford University Press."},{"issue":"3","key":"5631_CR29","doi-asserted-by":"publisher","first-page":"433","DOI":"10.2307\/40286178","volume":"13","author":"C Palmer","year":"1996","unstructured":"Palmer, C. (1996). Anatomy of a performance: Sources of musical expression. Music Perception, 13(3), 433\u2013453.","journal-title":"Music Perception"},{"key":"5631_CR30","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1146\/annurev.psych.48.1.115","volume":"48","author":"C Palmer","year":"1997","unstructured":"Palmer, C. (1997). Music performance. Annual Review of Psychology, 48, 115\u2013138.","journal-title":"Annual Review of Psychology"},{"key":"5631_CR31","unstructured":"Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In Proceedings of the 30th international conference on machine learning, Atlanta, GA, USA (pp. 1\u20139)."},{"key":"5631_CR32","unstructured":"Ramirez, R., & Hazan, A. (2004). Rule induction for expressive music performance modeling. In ECML workshop advances in inductive rule learning."},{"issue":"5","key":"5631_CR33","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1121\/1.404425","volume":"92","author":"BH Repp","year":"1992","unstructured":"Repp, B. H. (1992). Diversity and commonality in music performance\u2014An analysis of timing microstructure in Schumann\u2019s \u201cTr\u00e4umerei\u201d. Journal of the Acoustical Society of America, 92(5), 2546\u20132568.","journal-title":"Journal of the Acoustical Society of America"},{"key":"5631_CR34","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/BF00419658","volume":"56","author":"BH Repp","year":"1994","unstructured":"Repp, B. H. (1994). Relational invariance of expressive microstructure across global tempo changes in music performance: An exploratory study. Psychological Research, 56, 285\u2013292.","journal-title":"Psychological Research"},{"key":"5631_CR35","doi-asserted-by":"publisher","unstructured":"Repp, B. H., London, J., & Keller, P. E. (2013). Systematic distortions inmusicians\u2019 reproduction of cyclic three-intervalrhythms. Music Perception: An Interdisciplinary Journal, 30(3), 291\u2013305. doi: 10.1525\/mp.2012.30.3.291 . http:\/\/mp.ucpress.edu\/content\/30\/3\/291 .","DOI":"10.1525\/mp.2012.30.3.291"},{"key":"5631_CR36","unstructured":"Rod\u00e0, A., Schubert, E., De\u00a0Poli, G., & Canazza, S. (2015). Toward a musical Turing test for automatic music performance. In International symposium on computer music multidisciplinary research."},{"issue":"2","key":"5631_CR37","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.cpc.2009.09.018","volume":"181","author":"A Saltelli","year":"2010","unstructured":"Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181(2), 259\u2013270.","journal-title":"Computer Physics Communications"},{"issue":"11","key":"5631_CR38","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673\u20132681.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"5631_CR39","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1080\/14640748308402140","volume":"35A","author":"JA Sloboda","year":"1983","unstructured":"Sloboda, J. A. (1983). The communication of musical metre in piano performance. Quarterly Journal of Experimental Psychology, 35A, 377\u2013396.","journal-title":"Quarterly Journal of Experimental Psychology"},{"key":"5631_CR40","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929\u20131958.","journal-title":"Journal of Machine Learning Research"},{"key":"5631_CR41","volume-title":"Music and probability","author":"D Temperley","year":"2007","unstructured":"Temperley, D. (2007). Music and probability. Cambridge, MA: MIT Press."},{"key":"5631_CR42","unstructured":"Teramura, K., Okuma, H., Taniguchi, Y., Makimoto, S., & Maeda, S. (2008). Gaussian process regression for rendering music performance. In Proceedings of the 10th international conference on music perception and cognition (ICMPC 10), Sapporo, Japan."},{"issue":"1","key":"5631_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1525\/mp.2002.20.1.3","volume":"20","author":"R Timmers","year":"2002","unstructured":"Timmers, R., Ashley, R., Desain, P., Honing, H., & Windsor, L. (2002). Timing of ornaments in the theme of Beethoven\u2019s Paisiello Variations: Empirical data and a model. Music Perception, 20(1), 3\u201333.","journal-title":"Music Perception"},{"key":"5631_CR44","doi-asserted-by":"publisher","first-page":"3540","DOI":"10.1121\/1.402843","volume":"91","author":"N Todd","year":"1992","unstructured":"Todd, N. (1992). The dynamics of dynamics: A model of musical expression. Journal of the Acoustical Society of America, 91, 3540\u20133550.","journal-title":"Journal of the Acoustical Society of America"},{"key":"5631_CR45","unstructured":"van Herwaarden, S., Grachten, M., & de\u00a0Haas, W. B. (2014). Predicting expressive dynamics using neural networks. In Proceedings of the 15th conference of the international society for music information retrieval (pp. 47\u201352)."},{"issue":"4","key":"5631_CR46","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600\u2013612.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"1","key":"5631_CR47","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1076\/jnmr.31.1.37.8103","volume":"31","author":"G Widmer","year":"2002","unstructured":"Widmer, G. (2002). Machine discoveries: A few simple, robust local expression principles. Journal of New Music Research, 31(1), 37\u201350.","journal-title":"Journal of New Music Research"},{"issue":"2","key":"5631_CR48","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/S0004-3702(03)00016-X","volume":"146","author":"G Widmer","year":"2003","unstructured":"Widmer, G. (2003). Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence, 146(2), 129\u2013148.","journal-title":"Artificial Intelligence"},{"issue":"3","key":"5631_CR49","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/0929821042000317804","volume":"33","author":"G Widmer","year":"2004","unstructured":"Widmer, G., & Goebl, W. (2004). Computational models of expressive music performance: The state of the art. Journal of New Music Research, 33(3), 203\u2013216. doi: 10.1080\/0929821042000317804 .","journal-title":"Journal of New Music Research"},{"issue":"2","key":"5631_CR50","doi-asserted-by":"publisher","first-page":"127","DOI":"10.2307\/40285746","volume":"15","author":"WL Windsor","year":"1997","unstructured":"Windsor, W. L., & Clarke, E. F. (1997). Expressive timing and dynamics in real and artificial musical performances: Using an algorithm as an analytical tool. Music Perception, 15(2), 127\u2013152.","journal-title":"Music Perception"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-017-5631-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-017-5631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-017-5631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T22:26:15Z","timestamp":1719095175000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-017-5631-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,9]]},"references-count":50,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2017,6]]}},"alternative-id":["5631"],"URL":"https:\/\/doi.org\/10.1007\/s10994-017-5631-y","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2017,3,9]]}}}