Automatic Decision of Piano Fingering Based on a Hidden Markov Models
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2915-2920.
Abstract
This paper proposes a Hidden Markov Model (HMM)-based algorithm for automatic decision of piano fingering. We represent the positions and forms of hands and fingers as HMM states and model the resulted sequence of performed notes as emissions associated with HMM transitions. Optimal fingering decision is thus formulated as Viterbi search to find the most likely sequence of state transitions. The proposed algorithm models the required efforts in pressing a key with a finger followed by another key with another finger, and in two-dimensional positioning of fingers on the piano keyboard with diatonic and chromatic keys. Fundamental functionality of the algorithm was verified through experiments with real piano pieces. This framework can be extended to polyphonic music containing chords.
@inproceedings{ijcai2007p469,
title = {Automatic Decision of Piano Fingering Based on a Hidden Markov Models},
author = {Yonebayashi, Yuichiro and Kameoka, Hirokazu and Sagayama, Shigeki},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2915--2920},
year = {2007},
url = {https://ijcai.org/proceedings/2007/469},
}
TY - CPAPER TI - Automatic Decision of Piano Fingering Based on a Hidden Markov Models AU - Yonebayashi, Yuichiro AU - Kameoka, Hirokazu AU - Sagayama, Shigeki T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2915 EP - 2920 UR - https://ijcai.org/proceedings/2007/469 ER -Copied
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