The neural network model of music cognition ARTIST and applications to the WWW

Abstract

We present a simple ART2 neural network (NN) model, ARTIST, and show how after simple exposure to music and unsupervised learning, it is able to simulate high level perceptual and human cognitive abilities. Amongst other things, it is able to predict with a very high degree of accuracy how good a short musical sequence will sound to human ears. For this, ARTIST has to be exposed to the same kind of music as the listeners'. Such a model able to recover the rules of music aesthetics according to a particular musical environment, totally under control of the user, can have many applications to the distribution of music through the World Wide Web. The most straightforward application is to build an accurate profile of the user's musical preferences, based on the musical content itself. This should avoid the usual drawbacks of the current search engines and other "musical advisors", which base their advice on rigid musical style classifications, and are too general and impersonal. Other applications can range from assisted composition to interactive man-machine duet improvisation or the creation of online alternative versions of songs (remix).