NIPS Proceedingsβ

Unsupervised Structure Discovery for Semantic Analysis of Audio

Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Approaches to audio classification and retrieval tasks largely rely on detection-based discriminative models. We submit that such models make a simplistic assumption in mapping acoustics directly to semantics, whereas the actual process is likely more complex. We present a generative model that maps acoustics in a hierarchical manner to increasingly higher-level semantics. Our model has 2 layers with the first being generic sound units with no clear semantic associations, while the second layer attempts to find patterns over the generic sound units. We evaluate our model on a large-scale retrieval task from TRECVID 2011, and report significant improvements over standard baselines.