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Recurrent Neural Networks for Music Computation

Computer Science Department, Smith College, Northampton, Massachusetts 01063, USA
jfranklin{at}scinix.smith.edu

Some researchers in the computational sciences have considered music computation, including music reproduction and generation, as a dynamic system, i.e., a feedback process. The key element is that the state of the musical system depends on a history of past states. Recurrent (neural) networks have been deployed as models for learning musical processes. We first present a tutorial discussion of recurrent networks, covering those that have been used for music learning. Following this, we examine a thread of development of these recurrent networks for music computation that shows how more intricate music has been learned as the state of the art in recurrent networks improves. We present our findings that show that a long short-term memory recurrent network, with new representations that include music knowledge, can learn musical tasks, and can learn to reproduce long songs. Then, given a reharmonization of the chordal structure, it can generate an improvisation.

Key words: recurrent neural networks; computer music; music representation; LSTM
History: received January 2004; revised December 2004; accepted December 2004.