Giraldo, SergioOrtega, AriadnaPérez Carrillo, Alfonso Antonio, 1977-Ramírez, Rafael,1966-Waddell, GeorgeWilliamon, Aaron2019-03-142019-03-142018Giraldo S, Ortega A, Perez A, Ramirez R, Waddell G, Williamon A. Automatic assessment of violin performance using dynamic time warping classification. In: 26th Signal Processing and Communications Applications Conference; 2018 May 2-5; Izmir, Turkey. Nova Jersey: Institute of Electrical and Electronics Engineers; 2018. DOI: 10.1109/SIU.2018.8404556http://hdl.handle.net/10230/36825Comunicació presentada a: 26th Signal Processing and Communications Applications Conference, celebrada a Izmir, Turkey, del 2 al 5 de maig de 2018.The automatic assessment of music performance has become an area of special interest due to the increasing amount of technology-enhanced music learning systems. However, in most of these systems the assessment of the musical performance is based on the accuracy of onsets and pitch, paying little attention to other relevant aspects of performance. In this paper we present a preliminary study to assess the quality of violin performance using machine learning techniques. We collect recording examples of selected violin exercises varying from expert to amateur performances. We process the audio signal to extract features to train models using clustering based on Dynamic Time Warping distance. The quality of new performances is evaluated based on the level of match/miss-match to each of the recorded training examples.application/pdfeng© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at https://dx.doi.org/10.1109/SIU.2018.8404556info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/SIU.2018.8404556MusicFeature extractionMachine learningTrainingMusic information retrievalTime series analysisHidden Markov modelsinfo:eu-repo/semantics/openAccess