Analysing the Similarity of Album Art with Self-Organising Maps
Digital audio has become an ubiquitously available medium, and for many consumers, it is the major distribution and storage form of music, accounting for a growing share of record sales. However, handling the ever growing size of both private and commercial collections becomes increasingly difficult. Users are often overwhelmed by the seemingly countless number of music tracks available. Computer algorithms that can understand and interpret characteristics of music, and organise and recommend them for and to their users can thus be of great assistance.

Therefore, a magnitude of research projects has been devoted in the last decade to automatically to make the sound characteristics of music machine interpretable, to e.g. allow for automatic categorisation of music, or to recommend track which are similar to the ones a user likes.

However, music is an inherently multi-modal type of data, and increasingly also other modalities of music have attracted interest from the community. The analysis of song lyrics and other textual data, such as websites or biographies associated with artists, together with social network data, has probably attracted most research in this area.

Album covers are another dimensionality characteristic to the music - they are often carefully designed by artists to convey a message consistent with the music and image of a band. Studies have shown that customers use album cover art as a visual cue when browsing music in regular record stores. We thus present a study on similarities in album covers, and their relations to certain styles and genres of bands. To this end, we employ Self-Organising Maps together with various visualisation techniques to automatically organise a music collection, and compare the results obtained when using both features from the music and the album covers.