This is the first release of the 1.0 series. It is identical to 0.6.0.
BirdVoxDetect is a pre-trained deep learning system which detects flight calls from songbirds in audio recordings, and retrieves the corresponding species. It relies on per-channel energy normalization (PCEN) and context-adaptive convolutional neural networks (CA-CNN) for improved robustness to background noise. It is made available both as a Python library and as a command-line tool for Windows, OS X, and GNU/Linux.
Please visit our website for more information on the BirdVox project: https://wp.nyu.edu/birdvox
The main developer of BirdVoxDetect is Vincent Lostanlen, scientist at CNRS, the French national center for scientific research.
For any questions or announcements related to BirdVoxDetect, please refer to our discussion group: https://groups.google.com/g/birdvox
Please cite the following paper when using BirdVoxDetect in your work:
Robust Sound Event Detection in Bioacoustic Sensor Networks
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello
PLoS ONE 14(10): e0214168, 2019. DOI: https://doi.org/10.1371/journal.pone.0214168
As of v0.4, species classification in BirdVoxDetect relies on a taxonomical neural network (TaxoNet), which is distributed as part of the BirdVoxClassify package. For more details on TaxoNet, please refer to:
Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers
Jason Cramer, Vincent Lostanlen, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.