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A Syllable-Level Probabilistic Framework for Bird Species Identification

TitleA Syllable-Level Probabilistic Framework for Bird Species Identification
Publication TypeConference Paper
Year of Publication2009
AuthorsLakshminarayanan, B., R. Raich, and X. Z. Fern
Conference Name2009 International Conference on Machine Learning and Applications (ICMLA)2009 International Conference on Machine Learning and Applications
Pagination53 - 59
Date Published12/2009
PublisherIEEE
Conference LocationMiami, FL
ISBN Number978-0-7695-3926-3
Abstract

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%.

URLBayesian inference , Probabilistic modeling , audio classification , bird species identification
DOI10.1109/ICMLA.2009.79