Enabling empirical analysis of piano performance rehearsal with the Rach3 MIDI dataset

Abstract

The study of piano rehearsals can offer interesting insights into the strategies adopted by a pianist in order to learn, interpret and eventually perform musical pieces. The analysis of rehearsal processes requires computational methods that differ from those used for piano performance, due to challenges like mistakes, repetitions of musical segments, or forward and backward skips to sections in the piece. The scarcity of publicly available rehearsal data limits the empirical understanding of these challenges. We release the Rach3 MIDI Dataset, an openly available collection of MIDI files containing more than 750 hours of recordings of piano rehearsals and corresponding MusicXML scores by four pianists (3 advanced, 1 beginner), collected over a period of more than 4 years. This dataset records the progression of pianists learning new repertoire, as well as practicing familiar pieces, all in the Western Classical tradition. We describe the rehearsal piece identification process used for automatically labeling a portion of the data in this release. Furthermore, we use the Rach3 data to highlight several challenges and future research directions pertaining to the computational analysis of piano rehearsals, specifically symbolic rehearsal-to-score align-ment, rehearsal structure analysis, and automatic mistake identification.