Classification and ranking of environmental recordings to facilitate efficient bird surveys

This thesis contributes novel computer-assisted techniques to facilitating bird species surveys from a large number of environmental audio recordings. These techniques are applicable to both manual and automated recognition of bird species by removing irrelevant audio data and prioritising those relevant data for efficient bird species detection. This work also represents a significant step towards using automated techniques to support experts and the general public to explore and gain a better understanding of vocal species