Piano fingering is a creative and highly individualised task acquired by musicians progressively in their first music education
years. Pianists must learn to choose the order of fingers to play
the piano keys because scores do not have engraved finger and
hand movements as other technique elements. Numerous research
efforts have been conducted for automatic piano fingering based on
a previous dataset composed of 150 score excerpts fully annotated
by multiple expert annotators. However, most ...
Piano fingering is a creative and highly individualised task acquired by musicians progressively in their first music education
years. Pianists must learn to choose the order of fingers to play
the piano keys because scores do not have engraved finger and
hand movements as other technique elements. Numerous research
efforts have been conducted for automatic piano fingering based on
a previous dataset composed of 150 score excerpts fully annotated
by multiple expert annotators. However, most piano sheets include
partial annotations for problematic finger and hand movements.
We introduce a novel dataset for the task, the ThumbSet dataset,
containing 2523 pieces with partial and noisy annotations of piano
fingering crowdsourced from non-expert annotators. As part of our
methodology, we propose two autoregressive neural networks with
beam search decoding for modelling automatic piano fingering as
a sequence-to-sequence learning problem, considering the correlation between output finger labels. We design the first model with
the exact pitch representation of previous proposals. The second
model uses graph neural networks to more effectively represent
polyphony, whose treatment has been a common issue across previous studies. Finally, we finetune the models on the existing expert
annotations dataset. The evaluation shows that (1) we are able to
achieve high performance when training on the ThumbSet dataset
and that (2) the proposed models outperform the state-of-the-art
hidden Markov models and recurrent neural network baselines.
Code, dataset, models, and results are made available to enhance
the task reproducibility, including a new framework for evaluation
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