| dc.description.abstract | he Hawaiian monk seal (Neomonachus schauinslandi) is one of the most endangered marine 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 unexpected 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 automate 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%. | |