Detection of Piano Pedaling Techniques on the Sustain Pedal

Liang B, Fazekas G, Sandler MB. Detection of Piano Pedaling Techniques on the Sustain Pedal. In: AES Convention 143. Audio Engineering Society; 2017. Paper 9812. Available from: https://aes.org/publications/elibrary-page/?id=19209

@inproceedings{Liang2017_19209,
  author = {Liang, Beici and Fazekas, György and Sandler, Mark B.},
  title = {{Detection of Piano Pedaling Techniques on the Sustain Pedal}},
  booktitle = {AES Convention 143},
  note = {Paper 9812},
  year = {2017},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19209}
}

TY  - CPAPER
TI  - Detection of Piano Pedaling Techniques on the Sustain Pedal
AU  - Liang, Beici
AU  - Fazekas, György
AU  - Sandler, Mark B.
T2  - AES Convention 143
M1  - Paper 9812
PY  - 2017
DA  - 2017/10/06
UR  - https://aes.org/publications/elibrary-page/?id=19209
PB  - Audio Engineering Society
LA  - en
AB  - Automatic detection of piano pedaling techniques is challenging as it is comprised of subtle nuances of piano timbres. In this paper we address this problem on single notes using decision-tree-based support vector machines. Features are extracted from harmonics and residuals based on physical acoustics considerations and signal observations. We consider four distinct pedaling techniques on the sustain pedal (anticipatory full, anticipatory half, legato full, and legato half pedaling) and create a new isolated-note dataset consisting of different pitches and velocities for each pedaling technique plus notes played without pedal. Experiment shows the effectiveness of the designed features and the learned classifiers for discriminating pedaling techniques from the cross-validation trails.
ER  -