MusicRainbow: A New User Interface to Discover Artists Using Audio-based Similarity and Web-based Labeling

In this paper we present MusicRainbow which is a simple interface for discovering artists where colors encode different types of music. MusicRainbow is based on a new audiobased approach to compute artist similarity. This approach scores 15 percentage points higher in a genre classification task than the similarity computed on track level. Using a traveling salesman algorithm, similar artists are mapped near each other on a circular rainbow. Furthermore, we present a new approach of combining this audio-based information with information from the web. In particular, we label the rainbow and summarize the artists with words extracted from web pages related to the artists. We use different vocabularies for different hierarchical levels and heuristics to select the most descriptive labels. We conclude with a discussion of the results. The first impressions are very promising.