Later in the recording a warbler vocalisation is present but not identified by the templates as a Gang-gang call, suggesting that these first build recognition methods are working quite well at detecting the targeted species with some level of accuracy. The stronger calls are outlined in red, the weaker in blue with a decent margin of cross over where the mid-range calls are outlined in both red and blue. In the above photo you can see spectrogram output showing Gang-gang Cockatoo calls fading in and out as the bird flies over the automated recording unit. This is a spectrogram image produced in R-studio showing a Gang-gang Cockatoo calling in one of my field recordings, each coloured box represents a detection made by my templates, pretty encouraging results so far! – Photo is my own. Basically at this stage we’ve found out that we can actually make these automated recognisers for parrots, and I have had a lot of success making templates for Gang-gang Cockatoos. So back in my blog post about monitoring I did explain briefly why bioacoustics is good and what I was planning to do, but that was almost a year ago and by now I have all of my field data from various field trips, have analysed a sizable chunk of it, and I have built some preliminary recognisers using R-studio. What am I saying? Get back on track Kate… Making sense so far? I sure hope so because I can’t live update this content to reflect the level of understanding anyone currently has. This is a Song Meter 2+ (SM2+) made by Wildlife Acoustics that I deployed at Wyperfeld National Park in September 2016, these recordings were taken while the west half of the state was flooding so the recordings aren’t as full of birds as the others but something is better than nothing! – Photo is my own. Next up is altering the output a little to make the recognisers more specific (won’t detect the wrong species as the target species as often, but may miss detections of the species), or more sensitive (won’t miss any detections of the target species, but may identify the wrong species as the target species) and analysing the benefits and costs of each approach. In any case, as I already said, I have been exploring three of these automated recognition algorithms to work out how precise they are, and how likely they are to detect the target species if it is present by testing them across 22 species of parrots found in south-eastern Australia. Each algorithm matches the template to the field recording in a slightly different way, so I won’t go into that in this blog post, but that’s not a bad idea for a future post. My research aims to explore three algorithms that will generate species specific call templates, that is to say, they combine a set of pre-identified recordings of an animal and use the input to identify the target species within field recordings. This is a Bioacoustics Automated Recorder (BAR) made by Frontier Labs that I deployed at Chiltern Mt Pilot in December 2016, these recordings were full of bird life – Photo is my own.Īs per an earlier blog post of mine (*ahem* here) monitoring species for conservation is important and valuable, and there are emerging technologies that allow for improved monitoring outcomes. In short I am developing automated bioacoustics methods to monitor south-eastern Australian parrots, but really that’s a sentence full of jargon so let me break it down a little better for you. SO ANYWAY, I was trying to think of what to write about this fortnight and it came to mind that I actually haven’t really gone into what I am researching for my masters. Strap yourselves in friends because I’m about to take you on a wild ride through bird filled soundscapes, to the struggles of coding hills, and past the glorious fieldwork hills of yonder.įirst I should address that it’s been almost 3 months since my last blog post on January 4 th, I’ve been at conferences, on field work, frantically coding, and many more things that have seen my take a brief break from blog writing.
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