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 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/