Modeling Bends in Popular Music Guitar Tablatures
This work addresses the problem of automating guitar tablature arrangement for musicians and music producers, but it is incremental as it builds on existing methods for feature extraction and prediction.
The paper tackled predicting bend occurrences in guitar tablatures using high-level features, achieving an F1 score of 0.71 with a decision tree model on a dataset of 932 tablatures.
Tablature notation is widely used in popular music to transcribe and share guitar musical content. As a complement to standard score notation, tablatures transcribe performance gesture information including finger positions and a variety of guitar-specific playing techniques such as slides, hammer-on/pull-off or bends.This paper focuses on bends, which enable to progressively shift the pitch of a note, therefore circumventing physical limitations of the discrete fretted fingerboard. In this paper, we propose a set of 25 high-level features, computed for each note of the tablature, to study how bend occurrences can be predicted from their past and future short-term context. Experiments are performed on a corpus of 932 lead guitar tablatures of popular music and show that a decision tree successfully predicts bend occurrences with an F1 score of 0.71 anda limited amount of false positive predictions, demonstrating promising applications to assist the arrangement of non-guitar music into guitar tablatures.