Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset.
Sprache des Titels:
Englisch
Original Kurzfassung:
Several MIR applications require fine-grained note alignments between MIDI
performances and their musical scores for training and evaluation. However, large
and high-quality datasets with this kind of data are not available, and their manual
creation is a very time-consuming task that can only be performed by field experts.
In this paper, we evaluate state-of-the-art automatic note alignment models applied
to dataset generation. We increase the accuracy and reliability of the produced
alignments with models that flexibly leverage existing annotations such as beat or
measure alignments. We thoroughly evaluate these segment-constrained models
and use the best to create note alignments for the ASAP dataset, a large dataset of
solo piano MIDI performances beat-aligned to MusicXML scores. The resulting note
alignments are manually checked and publicly available at: https://github.com/CPJKU/
asap-dataset. The contributions of this paper are four-fold: (1) we extend the ASAP
dataset with reliable note alignments, thus creating (n)ASAP, the largest available fully
note-aligned dataset, comprising more than 7 M annotated notes and close to 100
hours of music; (2) we design, evaluate, and publish segment-constrained models
for note alignments that flexibly leverage existing annotations and significantly
outperform automatic models; (3) we design, evaluate, and publish unconstrained
automatic models for note alignment that produce results on par with the state of
the art; (4) we introduce Parangonada, a web-interface for visualizing and correcting
alignment annotations
Sprache der Kurzfassung:
Englisch
Journal:
Transactions of the International Society for Music Information Retrieval
Forschungs-