[MARMAM] New Bioacoustics Articles Published in Applied Acoustics Journal

Mahdi Esfahanian mesfahan at my.fau.edu
Fri Feb 10 00:04:00 PST 2017


Dear colleagues,

On behalf of all authors, I am pleased to announce the publication of our
new research in Applied Acoustics journal. Please feel free to let me know
if you don't have access to the paper.

Mahdi Esfahanian, Nurgun Erdol, Edmund Gerstein, Hanqi Zhuang,
"Two-stage detection of north Atlantic right whale upcalls using local
binary patterns and machine learning algorithms," Applied Acoustics,
Volume 120, May 2017, Pages 158-166,
http://dx.doi.org/10.1016/j.apacoust.2017.01.025

http://www.sciencedirect.com/science/article/pii/S0003682X17300774

The full paper is also accessible at the following link:
https://www.researchgate.net/publication/Mahdi_Esfahanian_Two-stage
<https://www.researchgate.net/publication/313159561_Two-stage_detection_of_north_Atlantic_right_whale_upcalls_using_local_binary_patterns_and_machine_learning_algorithms>
Abstract

In this paper, we investigate the effectiveness of two-stage classification
strategies in detecting north Atlantic right whale upcalls. Time-frequency
measurements of data from passive acoustic monitoring devices are evaluated
as images. Vocalization spectrograms are preprocessed for noise reduction
and tone removal. First stage of the algorithm eliminates non-upcalls by an
energy detection algorithm. In the second stage, two sets of features are
extracted from the remaining signals using contour-based and texture based
methods. The former is based on extraction of time–frequency features from
upcall contours, and the latter employs a Local Binary Pattern operator to
extract distinguishing texture features of the upcalls. Subsequently
evaluation phase is carried out by using several classifiers to assess the
effectiveness of both the contour-based and texture-based features for
upcall detection. Comparing ROC curves of machine learning algorithms
obtained from Cornell University’s dataset reveals that LBP features
improved performance accuracy up to 43% over time–frequency features.
Classifiers such as the Linear Discriminant Analysis, Support Vector
Machine, and TreeBagger achieve highest upcall detection rates with LBP
features.

-- 
Mahdi Esfahanian, Ph.D.
Department of Computer and Electrical Eng. and Computer Science
Florida Atlantic University
http://mahdiesfahanian.weebly.com/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.uvic.ca/pipermail/marmam/attachments/20170210/7267c0d6/attachment.html>


More information about the MARMAM mailing list