ATEPP: A DATASET OF AUTOMATICALLY TRANSCRIBED EXPRESSIVE PIANO PERFORMANCE

Abstract

Computational models of expressive piano performance rely on attributes like tempo, timing, dynamics and pedalling. Despite some promising models for performance assessment and performance rendering, results are limited by the scale, breadth and uniformity of existing datasets. In this paper, we present ATEPP, a dataset that contains 1000 hours of performances of standard piano repertoire by 49 world-renowned pianists, organized and aligned by compositions and movements for comparative studies. Scores in MusicXML format are also available for around half of the tracks. We first evaluate and verify the use of transcribed MIDI for representing expressive performance with a listening evaluation that involves recent transcription models. Then, the process of sourcing and curating the dataset is outlined, including composition entity resolution and a pipeline for audio matching and solo filtering. Finally, we conduct baseline experiments for performer identification and performance rendering on our datasets, demonstrating its potential in generalizing expressive features of individual performing style.