Modeling Bends in Popular Music Guitar Tablatures

TL;DR

This paper introduces a method to predict guitar bend occurrences in tablatures using high-level features and decision trees, aiding the transcription and arrangement of guitar music.

Contribution

It proposes a novel set of 25 features for predicting bends in guitar tablatures and demonstrates effective prediction with a decision tree model.

Findings

Decision tree achieves 0.71 F1 score in bend prediction.

Features effectively capture context for predicting guitar bends.

Method shows potential for assisting guitar music transcription.

Abstract

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.