A Neural Organist improvising baroque-style melodic variations
Dominik Hörnel, Peter Degenhardt · University of Michigan Library Repository · 1997
We present a multi-scale neural network system producing melodic variations in a style directly learned from music pieces of composers like Johann Sebastian Bach and Johann Pachelbel.Given any melody, the system first invents a four-part chorale harmonization and then improvises a variation of any chorale voice.Unlike earlier approaches to the learning of melodic structure, the system is able to learn and reproduce high-order structure like harmonic, motif and phrase structure in melodic sequences.This is achieved by using mutually interacting neural networks operating at different time scales, in combination with an unsupervised learning mechanism to classify and recognize musical structure.The results are complete music pieces, e.g. in the style of chorale partitas written by J. Pachelbel.Their quality has been judged by experts to be comparable to improvisations invented by an experienced human organist.