Abeßer, J.J.AbeßerDittmar, C.C.DittmarGrollmisch, S.S.GrollmischHasselhorn, J.J.HasselhornLehmann, A.A.Lehmann2022-03-122024-04-152022-03-122014https://publica.fraunhofer.de/handle/publica/387465In this paper, we introduce an approach for automated testing of music competency in rhythm production of ninth-grade and tenth-grade pupils. This work belongs in the larger context of modeling ratings of vocal and instrumental performances. Our approach relies on audio recordings from a specialized mobile application. Rhythmic features were extracted and used to train a machine-learning model which was targeted to approximate human ratings. Using two classes to assess the rhythmic performance, we obtained a mean class accuracy of 0.86.enautomatic music analysisconference paper