We investigate an approach to a music search engine that indexes music pieces based on related Web documents. This allows for searching for relevant music pieces by issuing descriptive textual queries. In this paper, we examine the effects of incorporating audio-based similarity into the text-based ranking process – either by directly modifying the retrieval process or by performing post-hoc audiobased re-ranking of the search results. The aim of this combination is to improve ranking quality by including relevant tracks that are left out by text-based retrieval approaches. Our evaluations show overall improvements but also expose limitations of these unsupervised approaches to combining sources. Evaluations are carried out on two collections, one large real-world collection containing about 35,000 tracks and on the CAL500 set. 1. MOTIVATION AND RELATE
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