Bird Identification from Timestamped, Geotagged Audio Recordings

Large-scale biodiversity monitoring would profit from automated solutions supporting or complementing human experts and citizen scientists. To foster their development, the yearly BirdCLEF scientific challenge compares approaches for identifying bird species in recorded vocalizations. The solution described in this work is based on an ensemble of Convolutional Neural Networks (CNNs) processing a mel spectrogram combined with Multi-Layer Perceptrons (MLPs) processing the recording date, time and geographic location. In BirdCLEF 2018, it achieved a mean average precision of 0.705 in detecting 1,500 South American bird species (0.785 for the foreground species), the second best entry to the challenge.

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