Predicting Marimba Stickings Using Long Short-Term Memory Neural Networks

Jet Kye Chong, Débora Corrêa

Research output: Chapter in Book/Conference paperConference paperpeer-review


In marimba music, ‘stickings’ are the choices of mallets used to strike each note. Stickings significantly influence both the physical facility and expressive quality of the music performance. Choosing ‘good’ stickings and evaluating one’s stickings are complex choices, often relying vaguely on trial-and-error. Machine learning (ML) approaches, particularly with advances in sequence-to-sequence techniques, have proved suited for similar complex classification problems, motivating their application in our study. We address the sticking problem by developing Long Short-Term Memory (LSTM) models to generate stickings in 4-mallet marimba music trained on exercises from Leigh Howard Stevens’ Method of Movement for Marimba. Model performance was measured under a range of metrics to account for multiple sticking possibilities, with LSTM models achieving a maximum average micro-accuracy of 97.3%. Finally, we discuss qualitative observations in sticking predictions and limitations of this study and provide direction for further development in this field.

Original languageEnglish
Title of host publicationAI 2022
Subtitle of host publicationAdvances in Artificial Intelligence - 35th Australasian Joint Conference, AI 2022, Proceedings
EditorsHaris Aziz, Débora Corrêa, Tim French
PublisherSpringer Science + Business Media
Number of pages14
ISBN (Print)9783031226946
Publication statusPublished - 2022
Event35th Australasian Joint Conference on Artificial Intelligence, AI 2022 - Perth, Australia
Duration: 5 Dec 20229 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13728 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th Australasian Joint Conference on Artificial Intelligence, AI 2022


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