A Dataset of rhythmic pattern reproductions and baseline automatic assessment system

Comunicació presentada a: 20th annual conference of the International Society for Music Information Retrieval (ISMIR) celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos.This work presents a novel dataset comprised of audio and jury evaluations for rhythmic pattern reproduction performances by students applying for a conservatory. Data was collected in-loco during entrance exams where students were asked to imitate a set of rhythmic patterns played by teachers. In addition to the pass or fail grades provided by the members of the jury during the exam sessions, a subset of the data was also evaluated by external annotators on a 4-level scale. A baseline automatic assessment system is presented to demonstrate the usefulness of the dataset. Preliminary results deliver an accuracy of 76% for a simple pass/fail logistic regression classifier and a mean average error of 0.59 for a linear regression grade estimator. The implementation is also made publicly available to serve as baseline for alternative assessments systems that may leverage the dataset.This work was conducted during a visiting scholar period at Universitat Pompeu Fabra, sponsored by the Capes Foundation within the Ministry of Education, Brazil (grant n. 88881.189929/2018-01). The dataset was collected during a project funded by the Scientific and Technological Research Council of Turkey, TUBITAK, Grant [215K017]

A Dataset of rhythmic pattern reproductions and baseline automatic assessment system

Comunicació presentada a: 20th annual conference of the International Society for Music Information Retrieval (ISMIR) celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos.This work presents a novel dataset comprised of audio and jury evaluations for rhythmic pattern reproduction performances by students applying for a conservatory. Data was collected in-loco during entrance exams where students were asked to imitate a set of rhythmic patterns played by teachers. In addition to the pass or fail grades provided by the members of the jury during the exam sessions, a subset of the data was also evaluated by external annotators on a 4-level scale. A baseline automatic assessment system is presented to demonstrate the usefulness of the dataset. Preliminary results deliver an accuracy of 76% for a simple pass/fail logistic regression classifier and a mean average error of 0.59 for a linear regression grade estimator. The implementation is also made publicly available to serve as baseline for alternative assessments systems that may leverage the dataset.This work was conducted during a visiting scholar period at Universitat Pompeu Fabra, sponsored by the Capes Foundation within the Ministry of Education, Brazil (grant n. 88881.189929/2018-01). The dataset was collected during a project funded by the Scientific and Technological Research Council of Turkey, TUBITAK, Grant [215K017]