Current AI tools for bioacoustics will split this audio into 2.5 second clips for analyses, and usually, these clips overlap each other to improve sound detection. In this example, your AI model will output roughly ๐ญ๐ฌ๐ฏ,๐ฒ๐ณ๐ต,๐ต๐ต๐ต ๐ฟ๐ผ๐๐ ๐ผ๐ณ ๐ฟ๐ฒ๐๐๐น๐๐ with each row containing a suggested species (or many species) per 2.5 seconds of audio. That's a lot of data.
Aside from the challenge of accurately labelling species in these data, youโll run into many challenges interpreting your results: โข Your model picked up a pied flycatcher, but was it stopping over during migration or breeding, or was it a false positive? โข Your AI picked up a thousand black grouse calls and you verified them, but is that one lonely grouse calling a thousand times? Or is it a lek of 20 calling at once? โข How many individual birds of each species do you have overall? Where are they? What are their age and sex distributions?
At Okala, we help organisations solve this data bottleneck and analyse large quantities of data with our Biodiversity Dashboard and team of AI engineers, expert ecologists and statisticians. If youโre trying to manage this yourself, hereโs what youโll need: โข AI tools like BirdNet or Perch (Google Bird Vocalization Classifier) โข A high-speed computer on-premises with expensive CPUs/GPUs to handle terabytes of data, or expertise in cloud computing and a fast internet connection โข An expert ecologist with knowledge of your landscape to review the species outputs and validate that the songs and calls make sense โข A statistician with geospatial expertise to turn results into actionable results like maps and abundance estimates Anticipate these challenges and prepare early, and youโll be able to transform your millions of data points into incredible conservation insights.
