Jazz Melody Generation from Recurrent Network Learning of Several Human Melodies

Judy A. Franklin, Smith College

Recurrent (neural) networks have been deployed as models for learning musical processes, by computational scientists who study processes such as dynamic systems. Over time, more intricate music has been learned as the state of the art in recurrent networks improves. One particular recurrent network, the Long Short-Term Memory (LSTM) network shows promise as a module that can learn long songs, and generate new songs. We are experimenting with using two LSTM modules to cooperatively learn several human melodies, based on the songs’ harmonic structures, and the feedback inherent in the network. We show that these networks can learn to reproduce four human melodies. We then introduce two harmonizations, constructed by us, that are given to the learned networks. i.e. we supply a reharmonization of the song structure, so as to generate new songs. We describe the reharmonizations, and show the new melodies that result. We also use a different harmonic structure from an existing jazz song not in the training set, to generate a new melody.
