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 ...
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.
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