  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. Experimental
results with the data set provided by the Cornell University Bioacoustics
Research Program reveal that classifiers show accuracy improvements of 3% to 4%
when using LBP features over time-frequency features. Classifiers such as the
Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve
high upcall detection rates with LBP features.
