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Unsupervised hierarchical structure induction for deeper semantic analysis of audio

Current audio analysis techniques rely on fairly shallow analysis of audio content, using symbols or patterns extracted directly from the observed acoustics. We hypothesize that the observed acoustics actually map to semantics in a hierarchical manner, and that the higher levels of this hierarchy correspond to increasingly higher-level semantics. In this paper, we present a model for deeper analysis of the observed acoustics, that induces a probabilistic tree structure depending on estimated constituent identities and contexts. Audio characterization using the deeper structure outperforms the standard shallow-feature based characterizations.