Metadata-Version: 2.1
Name: birdvoxdetect
Version: 0.6.0
Summary: Bioacoustic monitoring of nocturnal bird migration
Home-page: https://github.com/BirdVox/birdvoxdetect
Author: Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello
Author-email: vincent.lostanlen@nyu.edu
License: MIT
Project-URL: Source, https://github.com/BirdVox/birdvoxdetect
Project-URL: Tracker, https://github.com/BirdVox/birdvoxdetect/issues
Description: # BirdVoxDetect: detection and classification of flight calls
        
        [![PyPI](https://img.shields.io/badge/python-3.6-blue.svg)]()
        [![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://choosealicense.com/licenses/mit/)
        
        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 (>=2.2)
        * scikit-learn (==0.21.2)
        * birdvoxclassify (>=0.3)
        * 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:
        
            birdvoxdetect 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),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%
        
        
        ## Contact
        
        ### Official website
        Please visit our website for more information on the BirdVox project: [https://wp.nyu.edu/birdvox](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](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](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0214168&type=printable)**<br/>
        Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello<br/>
        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](https://www.justinsalamon.com/uploads/4/3/9/4/4394963/cramer_taxonet_icassp_2020.pdf)**<br/>
        Jason Cramer, Vincent Lostanlen, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello<br/>
        In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
        
Keywords: tfrecord
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: tests
