High Resolution Guitar Transcription via Domain Adaptation
Xavier Riley Drew Edwards Simon Dixon
Automatic music transcription (AMT) has achieved high accuracy for piano due
to the availability of large, high-quality datasets such as MAESTRO and MAPS,
but comparable datasets are not yet available for other instruments. In recent
work, however, it has been demonstrated that aligning scores to transcription
model activations can produce high quality AMT training data for instruments
other than piano. Focusing on the guitar, we refine this approach to training
on score data using a dataset of commercially available score-audio pairs. We
propose the use of a high-resolution piano transcription model to train a new
guitar transcription model. The resulting model obtains state-of-the-art
transcription results on GuitarSet in a zero-shot context, improving on
previously published methods.