Deep gradient reinforcement learning for music improvisation in cloud computing framework.

ABSTRACT: Artificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interactive and responsive music creation systems. Here, the musical structures train an RL agent to navigate the complex space of musical possibilities to provide improvisations. The melodic framework in the input musical data is initially identified using bi-directional gated recurrent units. The lyrical concepts such as notes, chords, and rhythms from the recognised framework are transformed into a format suitable for RL input. The deep gradient-based reinforcement learning technique used in this re

SUBMITTER: Alrowais F 

PROVIDER: S-EPMC11784531 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature