Music Icons: Procedural Glyphs for Audio Files

Nowadays, a personal music collection may comprise thousands of MP3 files. Visualization can help the user to gain an overview and to find similar songs inside so large a set. We describe a method to create icons from audio files in such a way that songs which the user considers similar receive similar icons. This allows visual data mining in standard directory listings of window-based operating systems. The icons consist of bloom-like shapes, whose form and color depend on eight parameters. These parameters are controlled through a neural net, the input of which are audio features that are extracted algorithmically from the MP3 files. To adapt the system to the user´s perception and interests, the neural net is initially trained with a small set of songs and icons. User studies done on the system demonstrate a strong perceptual relation between music and icons