A Mixed Observability Markov Decision Process Model for Musical Pitch

TL;DR

This paper introduces a novel application of Mixed Observability Markov Decision Processes (MOMDP) to model decision making in musical pitch environments, providing a new approach for intelligent agent interaction.

Contribution

It presents a new use case of MOMDP for modeling musical pitch interactions, bridging decision processes with music cognition.

Findings

MOMDP effectively models musical pitch decision making.

The approach simplifies complex musical interactions.

Potential for improved AI in music understanding.

Abstract

Partially observable Markov decision processes have been widely used to provide models for real-world decision making problems. In this paper, we will provide a method in which a slightly different version of them called Mixed observability Markov decision process, MOMDP, is going to join with our problem. Basically, we aim at offering a behavioural model for interaction of intelligent agents with musical pitch environment and we will show that how MOMDP can shed some light on building up a decision making model for musical pitch conveniently.