Bach 2.0 - generating classical music using recurrent neural networks

Abstract

The main incentive of this paper is to approach the sensitive subject of classical music synthesis in the form of musical scores by providing an analysis of different Recurrent Neural Network architectures. We will be discussing in a side-by-side comparison two of the most common neural network layers, namely Long-Short Term Memory and Gated Recurrent Unit, respectively, and study the effect of altering the global architecture meta-parameters, such as number of hidden neurons, layer count and number of epochs on the categorical accuracy and loss. A case study is performed on musical pieces composed by Johann Sebastian Bach and a method for estimating the repetition stride in a given musical piece is introduced. This is identified as the primary factor in optimizing the input length that must be fed during the training process.