title: Classifying multi-frequency fisheries acoustic data using a robust probabilistic classification technique.
abstract: A robust probabilistic classification technique, using expectation maximization of finite mixture models, is used to analyze multi-frequency fisheries acoustic data. The number of clusters is chosen using the Bayesian Information Criterion. Probabilities of membership to clusters are used to classify each sample. The utility of the technique is demonstrated using two examples: the Gulf of Alaska representing a low-diversity, well-known system; and the Mid-Atlantic Ridge, a species-rich, relatively unknown system.
authors.affiliation: School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, Washington 98195, USA.
authors.forename: C I H
authors.initials: CI
authors.lastname: Anderson
authors.forename: J K
authors.initials: JK
authors.lastname: Horne
authors.forename: J
authors.initials: J
authors.lastname: Boyle
mesh_terms: D000162:Acoustics; D000818:Animals; D016000:Cluster Analysis; D005398:Fisheries; D005399:Fishes; D006309:Hearing / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical; D011156:Population Density; D011336:Probability