Machine Learning Used to Decipher Lute Tablature
December 23, 2015
The Oxford Journal’s Early Music publication reveals a very specialized use of machine learning in, “Bring ‘Musicque into the Tableture’: Machine-Learning Models for Polyphonic Transcription of 16th-Century Lute Tablature” by musical researchers Reinier de Valk and Tillman Weyde. Note that this link will take you to the article’s abstract; to see the full piece, you’ll have to subscribe to the site. The abstract summarizes:
“A large corpus of music written in lute tablature, spanning some three-and-a-half centuries, has survived. This music has so far escaped systematic musicological research because of its notational format. Being a practical instruction for the player, tablature reveals very little of the polyphonic structure of the music it encodes—and is therefore relatively inaccessible to non-specialists. Automatic polyphonic transcription into modern music notation can help unlock the corpus to a larger audience and thus facilitate musicological research.
“In this study we present four variants of a machine-learning model for voice separation and duration reconstruction in 16th-century lute tablature. These models are intended to form the heart of an interactive system for automatic polyphonic transcription that can assist users in making editions tailored to their own preferences. Additionally, such models can provide new methods for analysing different aspects of polyphonic structure.”
The full article lays out the researchers’ modelling approaches and the advantages of each. They report their best model returns accuracy rates of 80 to 90 percent, so for modelers, it might be worth the $39 to check out the full article. We just think it’s nice to see machine learning used for such a unique and culturally valuable project.
Cynthia Murrell, December 23, 2015