CV | Ben McEwen

Name	Ben McEwen
Label	Postdoctoral Researcher
Email	benmcewen@outlook.com
Summary	Audio machine learning researcher with a focus on bioacoustics and uncertainty quantification. I am passionate about using machine learning to solve real-world problems, particularly in the field of biodiversity conservation.

2024

Active Few-Shot Learning for Rare Bioacoustic Feature Annotation

Ecological Informatics

Present a methodology that integrates few-shot learning and active learning for efficient analysis of sparse features
2024

Computational Bioacoustics for the Detection of Rare Acoustic Events (thesis)

Unversity of Canterbury

PhD dissertation related to the monitoring of rare acoustic events (particularly invasive species in New Zealand). This work explores the use of active few-shot learning for rare event annotation and well as prior research related to environmental noise and predictive tracking.
2023

An Improved Computational Bioacoustic Monitoring Approach for Detecting Sparse Features

Acoustical Society of America (JASA)

	Applied Research
	Machine Learning
	Uncertainty Quantification
	Signal Processing

	Associate Professor Dan Stowell
	Machine listening and computational bioacoustics. Associate Professor of AI & Biodiversity at Tilburg University and Naturalis Biodiversity Center dan.stowell@naturalis.nl

	Professor Stefanie Guschmidt
	Leads the Dynamics Systems lab and co-lead of the Listening Lab. Head of the Department of Mechanical Engineering at the University of Canterbury stefanie.gutschmidt@canterbury.ac.nz