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We improve upon query-by-example for content-based audio information retrieval by ranking items in a database based on semantic similarity, rather than acoustic similarity, to a query example. The retrieval system is based on semantic concept models that are learned from a training data set containing both audio examples and their text captions. Using the concept models, the audio tracks are mapped into a semantic feature space, where each dimension indicates the strength of the semantic concept. Audio retrieval is then based on ranking the database tracks by their similarity to the query in the semantic space. We experiment with both semantic- and acoustic-based retrieval systems on a sound effects database and show that the semantic-based system improves retrieval both quantitatively and qualitatively.