{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T01:26:13Z","timestamp":1699838773471},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2006,8]]},"abstract":"<jats:p> We propose novel machine learning methods for exploring the domain of music performance praxis. Based on simple measurements of timing and intensity in 12 recordings of a Schubert piano piece, short performance sequences are fed into a SOM algorithm in order to calculate 'performance archetypes'. The archetypes are labeled with letters and approximate string matching done by an evolutionary algorithm is applied to find similarities in the performances represented by these letters. We present a way of measuring each pianist's habit of playing similar phrases in similar ways and propose a ranking of the performers based on that. Finally, an experiment revealing common expression patterns is briefly described. <\/jats:p>","DOI":"10.1142\/s0218213006002795","type":"journal-article","created":{"date-parts":[[2006,8,2]],"date-time":"2006-08-02T08:21:22Z","timestamp":1154506882000},"page":"495-513","source":"Crossref","is-referenced-by-count":8,"title":["EXPLORING PIANIST PERFORMANCE STYLES WITH EVOLUTIONARY STRING MATCHING"],"prefix":"10.1142","volume":"15","author":[{"given":"S\u00d8REN TJAGVAD","family":"MADSEN","sequence":"first","affiliation":[{"name":"Austrian Research Institute for Artificial Intelligence, Freyung 6\/6, A-1010 Vienna, Austria"}]},{"given":"GERHARD","family":"WIDMER","sequence":"additional","affiliation":[{"name":"Department of Computational Perception, Johannes Kepler University, Altenberger Stra\u00dfe 69, A-4040 Linz, Austria"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012213564-4\/50015-9"},{"key":"rf2","first-page":"43","volume":"23","author":"de M\u00e1ntaras R. L.","journal-title":"AI Magazine"},{"key":"rf3","first-page":"111","volume":"24","author":"Widmer G.","journal-title":"AI Magazine"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012213564-4\/50014-7"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1121\/1.404425"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1076\/jnmr.30.1.39.7119"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-09562-1"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1162\/014892603322730514"},{"key":"rf14","volume-title":"Information Retrieval","author":"van Rijsbergen C. J.","year":"1979"},{"key":"rf15","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1613\/jair.374","volume":"7","author":"Nevill-Manning C. G.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(03)00016-X"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213006002795","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T22:32:03Z","timestamp":1565130723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213006002795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,8]]},"references-count":11,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2006,8]]}},"alternative-id":["10.1142\/S0218213006002795"],"URL":"https:\/\/doi.org\/10.1142\/s0218213006002795","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,8]]}}}