Bioacoustic monitoring of nocturnal bird migration
BirdVoxDetect: detection and classification of flight calls
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.
The simplest way to install BirdVoxDetect is by using the
pip package management system, which will also install the additional required dependencies
pip install birdvoxdetect
Note that birdvoxdetect requires:
- Python (3.6, 3.7, or 3.8)
- librosa (==0.7.0)
- tensorflow (>=2.2)
- scikit-learn (==0.21.2)
- birdvoxclassify (>=0.3)
From the command line
To analyze one file:
To analyze multiple files:
birdvoxdetect path/to/file1.wav path/to/file2.wav
To analyze one folder:
--clip-duration CLIP_DURATION, -d CLIP_DURATION Duration of the exported clips, expressed in seconds (fps). The default value is 1.0, that is, one second. We recommend values of 0.5 or above. --export-clips, -c Export detected events as audio clips in WAV format. --export-confidence, -C Export the time series of model confidence values of events in HDF5 format. --export-faults, -f Export list of sensor faults in CSV format. --export-logger, -l Export output of Python logger in TXT format. --output-dir OUTPUT_DIR, -o OUTPUT_DIR Directory to save the output file(s); The default value is the same directory as the input file(s). --predict-proba, -p Export output probabilities in JSON format. --quiet, -q Print less messages on screen. --suffix SUFFIX, -s SUFFIX String to append to the output filenames.The default value is the empty string. --threshold THRESHOLD, -t THRESHOLD Detection threshold, between 10 and 90. The default value is 50. Greater values lead to higher precision at the expense of a lower recall. --verbose, -v Print timestamps of detected events. --version, -V Print version number.
import birdvoxdetect as bvd df = bvd.process_file('path/to/file.wav')
df is a Pandas DataFrame with three columns: time, detection confidence, and species.
Below is a typical output from the test suite (file path
Time (hh:mm:ss),Detection confidence (%),Order,Order confidence (%),Family,Family confidence (%),Species (English name),Species (scientific name),Species (4-letter code),Species confidence (%) 0,00:00:08.78,70.15%,Passeriformes,100.00%,Turdidae,100.00%,Swainson's thrush,Catharus ustulatus,SWTH,99.28%
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.
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