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Audio retrieval by latent perceptual indexing

9 years 6 months ago
Audio retrieval by latent perceptual indexing
We present a query-by-example audio retrieval framework by indexing audio clips in a generic database as points in a latent perceptual space. First, feature-vectors extracted from the clips in the database are grouped into reference clusters using an unsupervised clustering technique. An audio clip-to-cluster matrix is constructed by keeping count of the number of features that are quantized into each of the reference clusters. By singular-value decomposition of this matrix, each audio clip of the database is mapped into a a point in the latent perceptual space. This is used for indexing the retrieval system. Since each of the initial reference clusters represents a specific perceptual quality in a perceptual space (similar to words that represent specific concepts in the semantic space), querying-by-example results in clips that have similar perceptual qualities. Subjective human evaluation indicates about 75% retrieval performance. Evaluation on semantic categories reveals that th...
Shiva Sundaram, Shrikanth Narayanan
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Shiva Sundaram, Shrikanth Narayanan
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