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A new algorithm for content-based audio information retrieval is introduced in this work. Assuming that there exist hidden acoustic topics and each audio clip is a mixture of those acoustic topics, we proposed a topic model that learns a probability distribution over a set of hidden topics of a given audio clip in an unsupervised manner. We use the Latent Dirichlet Allocation (LDA) method for the...
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...
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