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ICMLA
2009

A Syllable-Level Probabilistic Framework for Bird Species Identification

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A Syllable-Level Probabilistic Framework for Bird Species Identification
In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregating frame level features within a syllable. We characterize each syllable as a probability distribution of its frame level features. The independent frame independent syllable (IFIS) model allows us to distinguish syllables whose feature distributions are different from one another. The Markov chain frame independent syllable (MCFIS) model is introduced for scenarios where the temporal structure within the syllable provides significant amount of discriminative information. We derive the Bayes risk minimizing classifier for each model and show that it can be approximated as a nearest neighbour classifier. Our experiments indicate that the IFIS and MCFIS models achieve 88.26% and 90.61% correct classification rates, respectively, while the equivalent SVM implementation achieves 86.15%. Keywords-Probabilistic mo...
Balaji Lakshminarayanan, Raviv Raich, Xiaoli Fern
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICMLA
Authors Balaji Lakshminarayanan, Raviv Raich, Xiaoli Fern
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