A Data-Driven Analysis of Robust Automatic Piano Transcription
Drew Edwards Simon Dixon Emmanouil Benetos Akira Maezawa Yuta Kusaka
Algorithms for automatic piano transcription have improved dramatically in
recent years due to new datasets and modeling techniques. Recent developments
have focused primarily on adapting new neural network architectures, such as
the Transformer and Perceiver, in order to yield more accurate systems. In this
work, we study transcription systems from the perspective of their training
data. By measuring their performance on out-of-distribution annotated piano
data, we show how these models can severely overfit to acoustic properties of
the training data. We create a new set of audio for the MAESTRO dataset,
captured automatically in a professional studio recording environment via
Yamaha Disklavier playback. Using various data augmentation techniques when
training with the original and re-performed versions of the MAESTRO dataset, we
achieve state-of-the-art note-onset accuracy of 88.4 F1-score on the MAPS
dataset, without seeing any of its training data. We subsequently analyze these
data augmentation techniques in a series of ablation studies to better
understand their influence on the resulting models.