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Audio information retrieval using semantic similarity (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Barrington, Luke Chan, Antoni Lanckriet, Gert Turnbull, Douglas |
| Abstract | 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. Finally, we experiment with both semanticand acoustic-based retrieval systems on a sound effects database, and show that the retrieval of the semantic-based system improves both quantitatively and qualitatively. Index Terms — computer audition, audio retrieval, semantic similarity 1. |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Audio Retrieval Query Example Semantic Concept Semantic Similarity Content-based Audio Information Retrieval Sound Effect Database Training Data Semantic-based System Database Track Audio Information Retrieval Semantic Concept Model Concept Model Text Caption Retrieval System Semantic Feature Space Acoustic-based Retrieval System Semantic Space Audio Example Audio Track Acoustic Similarity Index Term Computer Audition |
| Content Type | Text |
