Learning Musical Structure and Style by Recognition, Prediction and Evolution
Dominik Hörnel, Thomas Ragg · 1996
We present an approach for modelling the recognition, prediction and evolution of musical structure and style based on unsupervised and supervised learning techniques. Unsupervised learning is proposed to classify musical structure leading to a distributed representation of structural elements. Prediction in time may be learned by neural networks. A multi-scale neural network model is presented which uses the above representation scheme to pick up and reproduce global structure from music examples. To improve the extraction of style-dependant features, an evolutionary neural network approach is suitable. All of these elements are integrated into the system MELONET II, which is able to produce and harmonize simple folk style melodies. 1 Prediction Various neural network models have been developed to successfully predict musical sequences occurring locally in time, e.g. harmonic progressions or melodic variations. They are not able however to capture musical structure occurring over lo...