  A new method for North Atlantic Right Whales (NARW) up-call detection using
Multimodel Deep Learning (MMDL) is presented in this paper. In this approach,
signals from passive acoustic sensors are first converted to spectrogram and
scalogram images, which are time-frequency representations of the signals.
These images are in turn used to train an MMDL detec-tor, consisting of
Convolutional Neural Networks (CNNs) and Stacked Auto Encoders (SAEs). Our
experimental studies revealed that CNNs work better with spectrograms and SAEs
with sca-lograms. Therefore in our experimental design, the CNNs are trained by
using spectrogram im-ages, and the SAEs are trained by using scalogram images.
A fusion mechanism is used to fuse the results from individual neural networks.
In this paper, the results obtained from the MMDL detector are compared with
those obtained from conventional machine learning algorithms trained with
handcraft features. It is shown that the performance of the MMDL detector is
sig-nificantly better than those of the representative conventional machine
learning methods in terms of up-call detection rate, non-up-call detection
rate, and false alarm rate.
