“Having this incredibly robust bird dataset – and feeding that into faster and more powerful machine-learning tools – enables Merlin to identify birds by sound now, when doing so seemed like a daunting challenge just a few years ago.” “Thousands of sound recordings train Merlin to recognize each bird species, and more than a billion bird observations in eBird tell Merlin which birds are likely to be present at a particular place and time,” said Drew Weber, Merlin project coordinator. Merlin’s pioneering approach to sound identification is powered by tens of thousands of citizen scientists who contributed their bird observations and sound recordings to eBird, the Cornell Lab’s global database. “So just like Merlin can identify a picture of a bird, it can now use this picture of a bird’s sound to make an ID.” “Each sound recording a user makes gets converted from a waveform to a spectrogram – a way to visualize the amplitude, frequency and duration of the sound,” Van Horn said.
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