Artificial Intelligence in Music Composing

Abstract

Previous work in music generation has mainly been focused on creating neural network for a single music style. More recent work has reported some remarkable success with different neural network architectures. My goal is to examine the outcome of multiples neural network architectures to various music styles. In this project, I introduce some basic probabilistic models based on estimated distribution of musical notes. Use complete piano roll representation to avoid agnostic learning and identify the feasibility of performing melody-wised and harmony-wised iteration through feedforward neural network and recurrent neural network.