Deep Learning for Music
This work addresses the challenge of creating realistic music for applications in entertainment and AI creativity, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of generating polyphonic music with both harmony and melody using deep neural networks, achieving results that are passable as human-composed music.
Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.