  Guitar tablature transcription is an important but understudied problem
within the field of music information retrieval. Traditional signal processing
approaches offer only limited performance on the task, and there is little
acoustic data with transcription labels for training machine learning models.
However, guitar transcription labels alone are more widely available in the
form of tablature, which is commonly shared among guitarists online. In this
work, a collection of symbolic tablature is leveraged to estimate the pairwise
likelihood of notes on the guitar. The output layer of a baseline tablature
transcription model is reformulated, such that an inhibition loss can be
incorporated to discourage the co-activation of unlikely note pairs. This
naturally enforces playability constraints for guitar, and yields tablature
which is more consistent with the symbolic data used to estimate pairwise
likelihoods. With this methodology, we show that symbolic tablature can be used
to shape the distribution of a tablature transcription model's predictions,
even when little acoustic data is available.
