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 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)
- birdvoxclassify
- h5py (>=2.9)
- librosa (==0.7.0)
- numpy (==1.16.4)
- pandas (==0.25.1)
- scikit-learn (==0.21.2)
- tensorflow (==1.15)
Usage
From the command line
To analyze one file:
python -m birdvoxdetect /path/to/file.wav
To analyze multiple files:
python -m birdvoxdetect /path/to/file1.wav /path/to/file2.wav
To analyze one folder:
python -m birdvoxdetect /path/to/folder
Optional arguments:
--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).
--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.
--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.
--suffix SUFFIX, -s SUFFIX
String to append to the output filenames.The default
value is the empty string.
--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.
--quiet, -q Print less messages on screen.
--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 fd79e55d-d3a3-4083-aba1-4f00b545c3d6.wav):
Time (hh:mm:ss) Species (4-letter code) Confidence (%)
0 00:00:08.78 SWTH 100.0
Contact
Vincent Lostanlen, Cornell Lab of Ornithology (@lostanlen on GitHub).
For more information on the BirdVox project, please visit our website: https://wp.nyu.edu/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
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