We are interested in automatically calculating music similarity based on the visual appearance of artists. By collecting a large set of promotional photographs featuring artists and using a state-of-the-art image annotation system, we show that we can successfully annotate artists with a large set of (genre) tags. This suggests that we can learn some notion of artist similarity based on visual appearance. Such a similarity measure provides us with a novel query-by-image retrieval paradigm for music discovery.

Researchers in the field of OMR (Optical Music Recognition) have acknowledged that the automatic transcription of medieval musical manuscripts is still an open problem [2, 3], mainly due to lack of standards in notation and the physical quality of the documents. Nonetheless, the amount of medieval musical manuscripts is so vast that the consensus seems to be that OMR can be a vital tool to help in the preserving and sharing of this information in digital format. In this paper we report our results on a preliminary approach to OMR of medieval plainchant manuscripts in square notation, at the symbol classification level, which produced good results in the recognition of eight basic symbols. Our preliminary approach consists of the preprocessing, segmentation, and classification stages. 1.

Abstract—While the perception of music tends to focus on our acoustic listening experience, the image of an artist can play a role in how we categorize (and thus judge) the artistic work. Based on a user study, we show that both album cover artwork and promotional photographs encode valuable information that helps place an artist into a musical context. We also describe a simple computer vision system that can predict music genre tags based on content-based image analysis. This suggests that we can automatically learn some notion of artist similarity based on visual appearance alone. Such visual information may be helpful for improving music discovery in terms of the quality of recommendations, the efficiency of the search process, and the aesthetics of the multimedia experience. I.