{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T02:14:17Z","timestamp":1692670457610},"reference-count":23,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2018,9]]},"abstract":" The automatic assessment of (student) music performance involves the characterization of the audio recordings and the modeling of human judgments. To build a computational model that provides a reliable assessment, the system must take into account various aspects of a performance including technical correctness and aesthetic standards. While some progress has been made in recent years, the search for an effective feature representation remains open-ended. In this study, we explore the possibility of using learned features from sparse coding. Specifically, we investigate three sets of features, namely a baseline set, a set of designed features, and a feature set learned with sparse coding. In addition, we compare the impact of two different input representations on the effectiveness of the learned features. The evaluation is performed on a dataset of annotated recordings of students playing snare exercises. The results imply the general viability of feature learning in the context of automatic assessment of music performances. <\/jats:p>","DOI":"10.1142\/s1793351x18400147","type":"journal-article","created":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T05:40:40Z","timestamp":1537508440000},"page":"315-333","source":"Crossref","is-referenced-by-count":4,"title":["Assessment of Percussive Music Performances with Feature Learning"],"prefix":"10.1142","volume":"12","author":[{"given":"Chih-Wei","family":"Wu","sequence":"first","affiliation":[{"name":"Center for Music Technology, Georgia Institute of Technology, 840 McMillan St, Atlanta, GA 30332, USA"}]},{"given":"Alexander","family":"Lerch","sequence":"additional","affiliation":[{"name":"Center for Music Technology, Georgia Institute of Technology, 840 McMillan St, Atlanta, GA 30332, USA"}]}],"member":"219","published-online":{"date-parts":[[2018,9,20]]},"reference":[{"key":"S1793351X18400147BIB001","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.48.1.115"},{"key":"S1793351X18400147BIB002","doi-asserted-by":"publisher","DOI":"10.1525\/mp.2016.33.5.662"},{"key":"S1793351X18400147BIB003","doi-asserted-by":"publisher","DOI":"10.1525\/mp.2003.21.1.21"},{"key":"S1793351X18400147BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-013-0258-3"},{"key":"S1793351X18400147BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2002.800560"},{"key":"S1793351X18400147BIB012","volume-title":"Software-Based Extraction of Objective Parameters from Music Performances","author":"Lerch A.","year":"2009"},{"key":"S1793351X18400147BIB013","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511811739.005"},{"key":"S1793351X18400147BIB014","volume-title":"The History of Music in Performance: The Art of Musical Interpretation from the Renaissance to Our Day","author":"Dorian F.","year":"1942"},{"key":"S1793351X18400147BIB015","volume-title":"Emotion and Meaning in Music","author":"Meyer L. B.","year":"1956"},{"issue":"1","key":"S1793351X18400147BIB016","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1214\/ss\/1009211806","volume":"14","author":"Beran J.","year":"1999","journal-title":"Statist. Sci"},{"key":"S1793351X18400147BIB017","doi-asserted-by":"publisher","DOI":"10.1002\/9781118393550"},{"key":"S1793351X18400147BIB018","doi-asserted-by":"publisher","DOI":"10.1007\/BF00419658"},{"key":"S1793351X18400147BIB019","doi-asserted-by":"publisher","DOI":"10.1121\/1.415889"},{"key":"S1793351X18400147BIB020","doi-asserted-by":"publisher","DOI":"10.1121\/1.1376133"},{"key":"S1793351X18400147BIB022","doi-asserted-by":"publisher","DOI":"10.1076\/jnmr.30.1.39.7119"},{"key":"S1793351X18400147BIB023","volume-title":"Psychology of Music","author":"Seashore C. E.","year":"1938"},{"key":"S1793351X18400147BIB028","first-page":"1096","author":"Lee H.","year":"2009","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"S1793351X18400147BIB035","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2362006"},{"key":"S1793351X18400147BIB036","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1980.1163420"},{"key":"S1793351X18400147BIB037","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000067"},{"key":"S1793351X18400147BIB040","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"S1793351X18400147BIB041","volume-title":"Statistics","author":"McClave J. T.","year":"2003","edition":"9"},{"key":"S1793351X18400147BIB042","series-title":"Information Science and Statistics","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. M.","year":"2006"}],"container-title":["International Journal of Semantic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793351X18400147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T04:51:26Z","timestamp":1565153486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793351X18400147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9]]},"references-count":23,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2018,9,20]]},"published-print":{"date-parts":[[2018,9]]}},"alternative-id":["10.1142\/S1793351X18400147"],"URL":"https:\/\/doi.org\/10.1142\/s1793351x18400147","relation":{},"ISSN":["1793-351X","1793-7108"],"issn-type":[{"value":"1793-351X","type":"print"},{"value":"1793-7108","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9]]}}}