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• W2148528520 abstract "In the recently proposed latent perceptual indexing of audio, a collection of clips is indexed using unit-document frequency measures between a set of reference clusters as units and the clips as the documents. The reference units are derived by clustering the bagof- feature vectors extracted from the whole audio library using an unsupervised clustering technique. Indexing is achieved through reduced-rank approximation (using singular-value decomposition) of the unit-document co-occurrence measure matrix that is obtained for the given set of reference clusters and the collection of audio clips. In our initial investigation, the k-means algorithm was used to derive the reference units. In this paper, we attempt to reduce the computation load requirements for the k-means algorithm and singular-value decomposition by randomly splitting the training data into smaller sized parts instead of working on it as a whole. We present results of classification experiments on the BBC sound effects library and our results indicate this approach can significantly reduce the computation time without significant loss in classification performance." @default.
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• W2148528520 date "2009-06-01" @default.
• W2148528520 modified "2023-10-03" @default.
• W2148528520 title "A divide-and-conquer approach to Latent Perceptual Indexing of audio for large Web 2.0 applications" @default.
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• W2148528520 doi "https://doi.org/10.1109/icme.2009.5202535" @default.
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