Dataset Open Access

MAST rhythm dataset

Baysal, Ozan; Bozkurt, Baris

Data collector(s)
Koker, Ogul; Guner, Burcin Bahadir

The MASTrhythm dataset is a collection of 3721 audio files cropped from recordings of conservatory entrance examinations in Turkey (summer 2015 and summer 2016). The entrance examination includes various tasks such as melody reproduction (see MASTmelody dataset), recognition of intervals and rhythmic patterns reproduction.

There are two categories for the files: reference recording (rhythmic patterns played by a jury member as the reference) and the recording of the candidate reproducing the pattern (referred as the "performance").

The candidate performances have been graded by three jury members who are teaching staff members of the conservatory. Grades are binary: pass, fail. This dataset includes only the samples for which all jury members agreed in grading with the same score. Hence, there are basically two categories for the performance files: i) performances graded as 'fail' by all the jury members , ii) performances graded as 'pass' by all the jury members.

For each candidate performance recording, the reference recording for that session is also available.

Naming convention:

The dataset is composed of audio files. All other information is coded in the file names: 'ref': reference recording, 'per': performance recording, 'fail': performance graded as 'fail', 'pass': performance graded as 'pass'

There exists 40 distinct rhythmic patterns. The ID for the melody makes up the first part of the file name. Examples:

'51_rhy1_per101559_fail.m4a': Rhythmic pattern ID: '51_rhy1', this is a candidate performance recording (candidate ID: 101559) graded as fail

'55_rhy2_ref280758.m4a': Rhythmic pattern ID: '55_rhy2', this is a reference recording (performed by a jury member) for candidate with ID: 280758.

Acknowledgement:

This dataset has been curated within the TUBITAK (The Scientific and Technological Research Council of Turkey) funded research project 1001-215K017 targeting development of automatic assessment tools for music performances.

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