The clustering process is also very effective at searching 'underneath' a classifier threshold, removing false positives and finding vocalisations that may have been missed. The red circles in the figure show where the owl vocalisations have been split away from other sounds.

In this paper we use a multi-stage machine learning pipeling (combining supervised and unsupervised methods) to reduce almost 3000 hours of environmental recordings into 10,116 annotations, of which 93% were correctly annotated individual notes of the target species.

Wasn’t expecting to see this on my walk yesterday - Noisy Pitta in suburban Brisbane!

They’re usually rainforest birds but migrate at this time of year and can show up in weird places.

Haven’t added any new species to my home patch list (now at 98) in ages, this will be hard to beat.

New pre-print! 🎙️🦉

Automated Note Annotation after Bioacoustic Classification: Unsupervised Clustering of Extracted Acoustic Features Improves Detection of a Cryptic Owl

papers.ssrn.com/sol3/papers....

#bioacoustics #machinelearning #conservation

First time birding in NZ. Cannot believe how many exotics there are everywhere. House sparrow chatter definitely most common noise of the day (Blackbird song close second).

Loved the Gannet colony!

Excited for #ESAus2024 this week! I'm speaking about birds, wine & machine learning on Thursday afternoon.

Finally joined BlueSky!

I'm a PhD candidate mostly working on #bioacoustics, #machinelearning & #conservation.

This black squiggle (a spectrogram) may not seem like much, but it’s the first Eastern Bristlebird heard in QLD in 5+ years found using acoustic recorders + a deep learning model.