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 bag-of-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 …

Date

2009

Authors

Shiva Sundaram, Shrikanth Narayanan

Conference

2009 IEEE International Conference on Multimedia and Expo

Pages

466-469

Publisher

IEEE