Abstract
Nowadays, consulting firms on environment propose to evaluate impacts of
transports and/or power production infrastructures on biodiversity using bioacoustic and adapted algorithms of signal processing. We present here our best algorithm (whose AUC score is 0.85\%). This is our contribution to the “Neural Information Processing Scaled for Bioacoustics ” (NIPS4B) workshop technical challenge 1 of NIPS 2013. Our objective was to obtain a bird-sound operational classification machine-learning model that environmental engineers (mostly or nithologists) could use to realise automatic inventories of acoustically active animals.
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