Erika Vilches

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In this work we explore the application of data mining techniques to the problem of acoustic recognition of bird species. Most bird song analysis tools produce a large amount of spectral and temporal attributes from the acoustic signal. The identification of distinctive features has become critical in resource constrained applications such as habitat(More)
Published in Agron. J. 100:98–104 (2008). doi:10.2134/agronj2006.0345 Copyright © 2008 by the American Society of Agronomy, 677 South Segoe Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information(More)
This study compares the ability of four classification methods to distinguish between songs of individual Mexican Antthrush Formicarius moniliger: self-organizing maps (SOMs), discriminant function analysis, fuzzy logic and hidden Markov models. Recordings were made under field conditions in a Mexican rainforest. Two types of data were analysed – recordings(More)
In this paper we propose the integration of Data Mining with Hidden Markov Models when applied to the problem of acoustic bird species recognition. We first show how each of them is applied on an individual manner, contrast their results and devise a model to combine them for targeted classifications. Previous work has shown that large collec- tions of(More)
In this paper, we explore the emergence of acoustic categories in sensor arrays. We describe a series experiments on the automatic categorization of species and individual birds using self-organizing maps. Experimental results showed that meaningful acoustic categories can arise as self-organizing processes in sensor arrays. In addition, we discuss how(More)
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