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Waveform of Bird Sounds


BirdVox, a collaboration between MARL and the Cornell Lab of Ornithology,  aims to investigate machine listening techniques for the automatic detection and classification of free-flying bird species from their vocalizations. The ultimate goal is to deploy a network of acoustic sensing devices for real-time monitoring of seasonal bird migratory patterns, particularly the determination of the precise timing of passage for each species.

Find out more

Further information including news updates, publications, and project collaborators is available at the BirdVox website.

Visit the BirdVox Website


Per-Channel Energy Normalization: Why and how
V. Lostanlen, J. Salamon, M. Cartwright, B. McFee, A. Farnsworth, S. Kelling, and J. P. Bello.
IEEE Signal Processing Letters, Nov. 2018.

Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection
V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. P. Bello
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Canada, Apr. 2018.

Fusing Shallow and Deep Learning for Bioacoustic Bird Species Classification
J. Salamon, J. P. Bello, A. Farnsworth and S. Kelling
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March 2017.

Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring
J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck and S. Kelling
PLOS ONE 11(11): e0166866, 2016.