Cornell researcher builds breakthrough machine learning toolkit for bioacoustics

Cornell researcher builds breakthrough machine learning toolkit for bioacoustics

A recent breakthrough was made in bio-acoustic deep learning techniques – a method for automated detection of animal sounds – at Cornell’s K. Lisa Yang Center for Conservation Bioacoustics. Dr. Shyam Madhusudhana, a postdoctoral researcher at the Laboratory of Ornithology, has built a toolkit allowing bio-acousticians to create complex audio recognition models with just a few lines of code.

The toolkit, Koogu, was used in a recent study that outperformed marine analysts in detecting blue whale D calls.

Blue whale D calls are calls of varying frequency produced by both male and female whales, unlike the well-known whale song which is only produced by males. While whale songs are often predictable and easily recognizable, D calls are erratic and produced less repetitively.


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