TY - GEN
T1 - The Rach3 Dataset: Towards Data-Driven Analysis of Piano Performance Rehearsal
AU - Cancino-Chacón, Carlos Eduardo
AU - Pilkov, Ivan
PY - 2024/3
Y1 - 2024/3
N2 - Musicians spend more time practicing than performing live,
but the process of rehearsal has been understudied. This paper introduces
a dataset for using AI and machine learning to address this gap. The
project observes the progression of pianists learning new repertoire over
long periods of time by recording their rehearsals, generating a comprehensive multimodal dataset, the Rach3 dataset, with video, audio, and
MIDI for computational analysis. This dataset will help investigating
the way in which advanced students and professional classical musicians,
particularly pianists, learn new music and develop their own expressive
interpretations of a piece.
AB - Musicians spend more time practicing than performing live,
but the process of rehearsal has been understudied. This paper introduces
a dataset for using AI and machine learning to address this gap. The
project observes the progression of pianists learning new repertoire over
long periods of time by recording their rehearsals, generating a comprehensive multimodal dataset, the Rach3 dataset, with video, audio, and
MIDI for computational analysis. This dataset will help investigating
the way in which advanced students and professional classical musicians,
particularly pianists, learn new music and develop their own expressive
interpretations of a piece.
UR - https://www.scopus.com/pages/publications/85189517450
U2 - 10.1007/978-3-031-56435-2_3
DO - 10.1007/978-3-031-56435-2_3
M3 - Conference proceedings
SN - 9783031564345
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 41
BT - Proceedings of the Multimedia Modeling Conference MMM24
A2 - Rudinac, Stevan
A2 - Hanjalic, Alan
A2 - Liem, Cynthia
A2 - Worring, Marcel
A2 - Jónsson, Björn Þór
A2 - Liu, Bei
A2 - Yamakata, Yoko
ER -