  Guitar tablature transcription (GTT) aims at automatically generating
symbolic representations from real solo guitar performances. Due to its
applications in education and musicology, GTT has gained traction in recent
years. However, GTT robustness has been limited due to the small size of
available datasets. Researchers have recently used synthetic data that
simulates guitar performances using pre-recorded or computer-generated tones
and can be automatically generated at large scales. The present study
complements these efforts by demonstrating that GTT robustness can be improved
by including synthetic training data created using recordings of real guitar
tones played with different audio effects. We evaluate our approach on a new
evaluation dataset with professional solo guitar performances that we composed
and collected, featuring a wide array of tones, chords, and scales.
