Bach in 2014: Music Composition with Recurrent Neural Network
This addresses music generation for computational creativity, but it is incremental as it applies existing methods like LSTM and RProp to a specific domain.
The paper tackled music composition by using a recurrent neural network with LSTM and RProp, showing that LSTM learns music structure to recreate pieces and that RProp outperforms BPTT in predicting existing music.
We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation through time (BPTT).