I graduated from Newcastle university in 2017 with an MMathStat in Mathematics and Statistics. In my final year, I worked with the Finance department of the car manufacturing company Nissan to help implement a base line model to predict the cost of building new cars.
In my spare time, I am a member of local wind bands and orchestras within the North East of England which perform on a regular basis, raising money for local charities.
PhD title
Acoustic Identification of Cetaceans using Deep Learning Techniques
Modelling cetacean (dolphins, whales and porpoises) population dynamics is paramount for effective conservation and population management. Cetaceans are prime candidates for modelling ecosystem change as they are at the top of the food chain within the ocean. Moreover, they can be used to assess the risk presented by anthropogenic activities.
Methodologies for cetacean research include passive acoustic monitoring (PAM) which allows for monitoring of cetacean occurrence and behaviour ecology through underwater recording. Due to high volumes of data collected and stored in PAM systems, there is a need for automated solutions that can detect and classify cetacean vocalisations. Current technologies provide identification of cetacean species using their vocalisations but do not harness deep learning techniques and/or use signature whistles to identify individuals within a species.
This project is designing a system that will detect and classify white-beaked dolphin whistles using signal processing and deep learning techniques. White-beaked dolphins migrate through the North-East of England between June and October each year and there is evidence to suggest the whistles that they produce are distinct, meaning they can be identified by these whistles - known as signature whistles. There is also evidence from a recent study to suggest their health is in decline, thus the monitoring of these groups is paramount.
