💾 Inspect and (maybe) Upload Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection

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Birdvox-Full-Night

Description of the Data:

35402 flight calls in total, among ~25 different species of passerines. The annotation process took 102 hours.

Data Download location:

https://wp.nyu.edu/birdvox/birdvox-full-night/

Article Abstract:

This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors. We find a large performance gap between energy-based detection functions and data-driven machine listening. The best model is a deep convolutional neural network trained with data augmentation. We correlate recall with the density of flight calls over time and frequency and identify the main causes of false alarm.

Citation

V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling and J. P. Bello, "Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, 2018, pp. 266-270, doi: 10.1109/ICASSP.2018.8461410.