| dc.description.abstract | The Hawaiian monk seal (Neomonachus schauinslandi) is one of the most endangered ma-
rine mammals in the world. With an estimated population of 1,570 individuals remaining
in the wild, the species have an urgent conservation priority. For effective management of
threatened and endangered species, monitoring can prove crucial for detecting possible unex-
pected changing in their environment. The majority of the Hawaiian monk seal’s population
exist in the marine conservation area located in the Northwest Hawaiian Islands. Therefore,
non-invasive methods, such as passive acoustic monitoring, could be a possible solution to
monitoring the species in their habitat.
However, analyzing the vast quantities of data obtained with passive acoustic monitoring is
slow and labor-intensive. In this thesis, we present a deep learning-based approach to auto-
mate the process. With the use of a Region-Based Convolutional Neural Network (R-CNN)
to detect the presence of a Hawaiian monk seal’s underwater vocalization. Followed by a
Convolutional Neural Network to classify the vocalizations into six previously defined call
types. The detector managed to achieve an AP of 0.35 and AP50 of 0.55, meanwhile the
classifier achieved an accuracy of 93.3% | |