Merge pull request #576 from kahst:extend-docs

fixes birdnetr and faq

docs/best-practices.rst

@@ -2,7 +2,7 @@ Best practices
 ==============
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    best-practices/species-lists
    best-practices/segment-review

docs/birdnetr.rst

@@ -0,0 +1,14 @@
+BirdNET in R
+============
+
+We do also provide a BirdNET package for R, which allows you to analyze audio recordings directly in R.
+
+**birdnetR** is geared towards providing a robust workflow for ecological data analysis in bioacoustic projects. 
+While it covers essential functionalities, it doesn’t include all the features found in BirdNET-Analyzer. 
+Some features might only be available in the BirdNET Analyzer and not in this package.
+
+.. note:: Please note that birdnetR is under active development, so you might encounter changes that could affect your current workflow. We recommend checking for updates regularly.
+
+See our website for more information: 
+
+`https://birdnet-team.github.io/birdnetR/index.html <https://birdnet-team.github.io/birdnetR/index.html>`_

docs/faq.rst

@@ -1,4 +1,57 @@
 FAQ
 ===
 
-We will answer frequently asked questions here.
+We will answer frequently asked questions here. If you have a question that is not answered here, please let us know at `[email protected] <mailto:[email protected]>`_.
+
+What is BirdNET-Analyzer?
+-------------------------
+
+BirdNET-Analyzer is a tool for analyzing bird sounds using machine learning models. It can identify bird species from audio recordings and provides various functionalities for training custom classifiers, extracting segments, and reviewing results.
+
+How do I install BirdNET-Analyzer?
+----------------------------------
+
+BirdNET-Analyzer can be installed using different methods, including:
+
+- | **Raven Pro**: Follow the instructions provided in the Raven Pro documentation.
+- | **Python Package**: Install via pip using `pip install birdnet-analyzer`.
+- | **Command Line**: Download the repository and run the scripts from the command line.
+- | **GUI**: Download the GUI version from the website and follow the installation instructions.
+
+How do I create a custom species list?
+--------------------------------------
+
+To create a custom species list, follow these steps:
+
+- | Copy species names from the labels file of each model, found in the checkpoints folder (e.g., `checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Labels.txt`).
+- | Ensure species names are in the format `scientific name_common name`.
+- | Use the `species.py` script to generate a species list for a given location and time.
+
+How do I use the "segments" function in the GUI?
+------------------------------------------------
+
+The "segments" function in the GUI allows you to create a collection of species-specific predictions that exceed a user-defined confidence value. To use this function:
+
+- | Select audio, result, and output directories.
+- | Set additional parameters such as the minimum confidence value, the maximum number of segments per species, the audio speed, and the segment length.
+- | Start the extraction process. BirdNET will create subfolders for each identified species and save audio clips of the corresponding recordings.
+
+How do I review the extracted segments?
+---------------------------------------
+
+The review tab in the GUI allows you to systematically review and label the extracted segments. It provides tools for visualizing spectrograms, listening to audio segments, and categorizing them as positive or negative detections. You can also generate logistic regression plots to visualize the relationship between confidence values and the likelihood of correct detections.
+
+What are BirdNET confidence values?
+-----------------------------------
+
+BirdNET confidence values are a measure of the algorithm's prediction reliability. They are not probabilities and are not directly transferable between different species or recording conditions. It is recommended to start with the highest confidence scores and work down to the lower scores when reviewing results.
+
+What licenses are used in BirdNET-Analyzer?
+-------------------------------------------
+
+BirdNET-Analyzer source code is released under the MIT License. The models used in the project are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Please review and adhere to the specific license terms provided with each model.
+Custom models trained with BirdNET-Analyzer are also subject to the same licensing terms.
+
+.. note:: Please note that all educational and research purposes are considered non-commercial use and it is therefore freely permitted to use BirdNET models in any way.
+
+Please get in touch if you have any questions or need further assistance. 

docs/index.rst

@@ -4,7 +4,7 @@ BirdNET-Analyzer Documentation
 Welcome to the BirdNET-Analyzer documentation! This guide provides detailed information on installing, configuring, and using BirdNET-Analyzer.
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Contents:
 
    installation
@@ -13,6 +13,7 @@ Welcome to the BirdNET-Analyzer documentation! This guide provides detailed info
    best-practices
    faq
    showroom
+   birdnetr
 
 Introduction
 ------------
@@ -24,7 +25,7 @@ Get started by listening to this AI-generated introduction of the BirdNET-Analyz
 .. raw:: html
 
     <audio controls>
-      <source src="../_static/BirdNET_Guide-Introduction-NotebookLM.mp3" type="audio/mpeg">
+      <source src="_static/BirdNET_Guide-Introduction-NotebookLM.mp3" type="audio/mpeg">
       Your browser does not support the audio element.
     </audio>
 
@@ -71,9 +72,9 @@ You can access the map here: `Open projects map <https://kahst.github.io/BirdNET
 License
 -------
 
-   **Source Code**: The source code for this project is licensed under the `MIT License <https://opensource.org/licenses/MIT>`_
+**Source Code**: The source code for this project is licensed under the `MIT License <https://opensource.org/licenses/MIT>`_
 
-   **Models**: The models used in this project are licensed under the `Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_
+**Models**: The models used in this project are licensed under the `Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_
 
 Please ensure you review and adhere to the specific license terms provided with each model.