Generating Piano Practice Policy with a Gaussian Process

• URL: http://arxiv.org/abs/2406.04812v1
• Date: Fri, 7 Jun 2024 10:27:07 GMT
• Title: Generating Piano Practice Policy with a Gaussian Process
• Authors: Alexandra Moringen, Elad Vromen, Helge Ritter, Jason Friedman,
• Abstract summary: We present a modeling framework to guide the human learner through the learning process by choosing the practice modes generated by a policy model. The proposed policy model is trained to approximate the expert-learner interaction during a practice session.
• Score: 42.41481706562645
• License: http://creativecommons.org/licenses/by/4.0/
• 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 often unavailable. In this work, we present a modeling framework to guide the human learner through the learning process by choosing the practice modes generated by a policy model. To this end, we present a computational architecture building on a Gaussian process that incorporates 1) the learner state, 2) a policy that selects a suitable practice mode, 3) performance evaluation, and 4) expert knowledge. The proposed policy model is trained to approximate the expert-learner interaction during a practice session. In our future work, we will test different Bayesian optimization techniques, e.g., different acquisition functions, and evaluate their effect on the learning progress.

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