  In this paper, we develop a novel method based on machine-learning and image
processing to identify North Atlantic right whale (NARW) up-calls in the
presence of high levels of ambient and interfering noise. We apply a continuous
region algorithm on the spectrogram to extract the regions of interest, and
then use grid masking techniques to generate a small feature set that is then
used in an artificial neural network classifier to identify the NARW up-calls.
It is shown that the proposed technique is effective in detecting and capturing
even very faint up-calls, in the presence of ambient and interfering noises.
The method is evaluated on a dataset recorded in Massachusetts Bay, United
States. The dataset includes 20000 sound clips for training, and 10000 sound
clips for testing. The results show that the proposed technique can achieve an
error rate of less than FPR = 4.5% for a 90% true positive rate.
