We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing.
Some authors have experimented with encoding musical structure into a hierarchical RNN with layers dedicated to generated the melody, drums, and chords REF .
Song From PI: A Musically Plausible Network for Pop Music Generation
ArXiv
ArXiv
Computer Science