Generating Piano Practice Policy with a Gaussian Process
Alexandra Moringen, Elad Vromen, Helge Ritter, Jason Friedman

TL;DR
This paper introduces a Gaussian process-based framework to optimize piano practice sessions by dynamically selecting practice modes tailored to individual learners, aiming to enhance learning efficiency without constant human supervision.
Contribution
It presents a novel computational architecture that models learner progress and guides practice mode selection using Gaussian processes, integrating expert knowledge and performance feedback.
Findings
Framework models learner state and practice mode selection
Incorporates expert knowledge into the learning process
Future work includes testing Bayesian optimization techniques
Abstract
A typical process of learning to play a piece on a piano consists of a progression through a series of practice units that focus on individual dimensions of the skill, the so-called practice modes. Practice modes in learning to play music comprise a particularly large set of possibilities, such as hand coordination, posture, articulation, ability to read a music score, correct timing or pitch, etc. Self-guided practice is known to be suboptimal, and a model that schedules optimal practice to maximize a learner's progress still does not exist. Because we each learn differently and there are many choices for possible piano practice tasks and methods, the set of practice modes should be dynamically adapted to the human learner, a process typically guided by a teacher. However, having a human teacher guide individual practice is not always feasible since it is time-consuming, expensive, and…
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Taxonomy
TopicsMusic Technology and Sound Studies
MethodsSparse Evolutionary Training · Focus · Gaussian Process
