Package: birdvoxdetect
BirdVoxDetect: detection and classification of flight callsBirdVoxDetect 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.InstallationThe simplest way to install BirdVoxDetect is by using thepippackage management system, which will also install the additional required dependencies
if needed.pip install birdvoxdetectNote 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)h5pypandasUsageFrom the command lineTo analyze one file:birdvoxdetect path/to/file.wavTo analyze multiple files:birdvoxdetect path/to/file1.wav path/to/file2.wavTo analyze one folder:birdvoxdetect path/to/folderOn Windows:birdvoxdetect path\to\folderOptional 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 PythonCall syntax:import birdvoxdetect as bvd    
df = bvd.process_file('path/to/file.wav')dfis a Pandas DataFrame with three columns: time, detection confidence, and species.Below is a typical output from the test suite (file pathtests/data/audio/fd79e55d-d3a3-4083-aba1-4f00b545c3d6.wav):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%ContactOfficial websitePlease visit our website for more information on the BirdVox project:https://wp.nyu.edu/birdvoxThe main developer of BirdVoxDetect is Vincent Lostanlen, scientist at CNRS, the French national center for scientific research.Discussion groupFor any questions or announcements related to BirdVoxDetect, please refer to our discussion group:https://groups.google.com/g/birdvoxReferencesPlease cite the following paper when using BirdVoxDetect in your work:Robust Sound Event Detection in Bioacoustic Sensor NetworksVincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo BelloPLoS ONE 14(10): e0214168, 2019. DOI:https://doi.org/10.1371/journal.pone.0214168As 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 ClassifiersJason Cramer, Vincent Lostanlen, Andrew Farnsworth, Justin Salamon, and Juan Pablo BelloIn IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.