Bioacoustic monitoring of nocturnal bird migration
Project description
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.
Installation
The simplest way to install BirdVoxDetect is by using the pip package management system, which will also install the additional required dependencies
if needed.
pip install birdvoxdetect
Note that birdvoxdetect requires:
- Python (3.6, 3.7, or 3.8)
- librosa (==0.7.0)
- tensorflow (>=2)
- scikit-learn (==0.21.2)
- birdvoxclassify (0.2)
- h5py
- pandas
Usage
From the command line
To analyze one file:
birdvoxdetect path/to/file.wav
To analyze multiple files:
birdvoxdetect path/to/file1.wav path/to/file2.wav
To analyze one folder:
birdvoxdetect path/to/folder
On Windows:
birdxodetect path\to\folder
Optional arguments:
--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.
From Python
Call syntax:
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 tests/data/audio/fd79e55d-d3a3-4083-aba1-4f00b545c3d6.wav):
Time (hh:mm:ss) Species (4-letter code) Family Order Confidence (%)
00:00:08.78 SWTH Turdidae Passeriforme 70.14839
Contact
Official website
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.
Discussion group
For any questions or announcements related to BirdVoxDetect, please refer to our discussion group: https://groups.google.com/g/birdvox
References
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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