  Biodiversity monitoring using audio recordings is achievable at a truly
global scale via large-scale deployment of inexpensive, unattended recording
stations or by large-scale crowdsourcing using recording and species
recognition on mobile devices. The ability, however, to reliably identify
vocalising animal species is limited by the fact that acoustic signatures of
interest in such recordings are typically embedded in a diverse and complex
acoustic background. To avoid the problems associated with modelling such
backgrounds, we build generative models of bird sounds and use the concept of
novelty detection to screen recordings to detect sections of data which are
likely bird vocalisations. We present detection results against various
acoustic environments and different signal-to-noise ratios. We discuss the
issues related to selecting the cost function and setting detection thresholds
in such algorithms. Our methods are designed to be scalable and automatically
applicable to arbitrary selections of species depending on the specific
geographic region and time period of deployment.
