Metadata-Version: 2.1
Name: birdvoxdetect
Version: 0.3.0a1
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/)
        [![Build Status](https://travis-ci.org/BirdVox/birdvoxdetect.svg?branch=master)](https://travis-ci.org/BirdVox/birdvoxdetect)
        
        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
                                  eventsin HDF5 format.
            --threshold THRESHOLD, -t THRESHOLD
                                  Detection threshold, between 10 and 90. The default
                                  value is 30. 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](https://wp.nyu.edu/birdvox)
        
        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
        
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
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: tests
