Probabilistic models for classification of bioacoustic data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/1c18dk74c

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  • Probabilistic models have been successfully applied for a wide variety of problems, such as but not limited to information retrieval, computer vision, bio-informatics and speech processing. Probabilistic models allow us to encode our assumptions about the data in an elegant fashion and enable us to perform machine learning tasks such as classification and clustering in a principled manner. Probabilistic models for bio-acoustic data help in identifying interesting patterns in the data (for instance, the species-specific vocabulary), as well as species identification (classification) in recordings where the label is not available. The focus of this thesis is to develop efficient inference techniques for existing models, as well as develop probabilistic models tailored to bioacoustic data. First, we develop inference algorithms for the supervised latent Dirichlet allocation (LDA) model. We present collapsed variational Bayes, collapsed Gibbs sampling and maximum-a-posteriori (MAP) inference for parameter estimation and classification in supervised LDA. We provide an empirical evaluation of the trade-off between computational complexity and classification performance of the inference methods for supervised LDA, on audio classification (species identification in this context)as well as image classification and document classification tasks. Next, we present novel probabilistic models for bird sound recordings, that can capture temporal structure at different hierarchical levels, and model additional information such as the duration and frequency of vocalizations. We present a non-parametric density estimation technique for parameter estimation and show that the MAP classifier for our models can be interpreted as a weighted nearest neighbor classifier. We provide an experimental comparison between the proposed models and a support vector machine based approach, using bird sound recordings from the Cornell Macaulay library.
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  • description.provenance : Submitted by Balaji Lakshminarayanan (lakshmba@onid.orst.edu) on 2010-12-28T22:22:09Z No. of bitstreams: 1 LakshminarayananBalaji2011.pdf: 910569 bytes, checksum: 58df1b1ee91b6274015106afdcddd4dd (MD5)
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  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2010-12-30T18:00:25Z (GMT) No. of bitstreams: 1 LakshminarayananBalaji2011.pdf: 910569 bytes, checksum: 58df1b1ee91b6274015106afdcddd4dd (MD5)
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