diff --git a/README.adoc b/README.adoc
index e970a999..d5cd387e 100644
--- a/README.adoc
+++ b/README.adoc
@@ -113,41 +113,6 @@ We also have a discussion forum on https://reddit.com/r/BirdNET_Analyzer[Reddit]
== Contents
toc::[]
-== Model version update
-
-[discrete]
-==== V2.4, June 2023
-
-* more than 6,000 species worldwide
-* covers frequencies from 0 Hz to 15 kHz with two-channel spectrogram (one for low and one for high frequencies)
-* 0.826 GFLOPs, 50.5 MB as FP32
-* enhanced and optimized metadata model
-* global selection of species (birds and non-birds) with 6,522 classes (incl. 10 non-event classes)
-
-You can find a list of previous versions here: https://github.com/kahst/BirdNET-Analyzer/tree/main/checkpoints[BirdNET-Analyzer Model Version History]
-
-[discrete]
-==== Species range model V2.4 - V2, Jan 2024
-
-* updated species range model based on eBird data
-* more accurate (spatial) species range prediction
-* slightly increased long-tail distribution in the temporal resolution
-* see https://github.com/kahst/BirdNET-Analyzer/discussions/234[this discussion post] for more details
-
-== Technical Details
-
-Model V2.4 uses the following settings:
-
-* 48 kHz sampling rate (we up- and downsample automatically and can deal with artifacts from lower sampling rates)
-* we compute 2 mel spectrograms as input for the convolutional neural network:
-** first one has fmin = 0 Hz and fmax = 3000; nfft = 2048; hop size = 278; 96 mel bins
-** second one has fmin = 500 Hz and fmax = 15 kHz; nfft = 1024; hop size = 280; 96 mel bins
-* both spectrograms have a final resolution of 96x511 pixels
-* raw audio will be normalized between -1 and 1 before spectrogram conversion
-* we use non-linear magnitude scaling as mentioned in http://ceur-ws.org/Vol-2125/paper_181.pdf[Schlüter 2018]
-* V2.4 uses an EfficienNetB0-like backbone with a final embedding size of 1024
-* See https://github.com/kahst/BirdNET-Analyzer/issues/177#issuecomment-1772538736[this comment] for more details
-
== Usage guide
This document provides instructions for downloading and installing the GUI, and conducting some of the most common types of analyses. Within the document, a link is provided to download example sound files that can be used for practice.
@@ -179,6 +144,8 @@ Learn more at: https://haikubox.com[HaikuBox.com]
Built on the TFLite version of BirdNET, this project uses pre-built TFLite binaries for Raspberry Pi to run on-device sound analyses.
It is able to recognize bird sounds from a USB sound card in realtime and share its data with the rest of the world.
+Note: You can find the most up-to-date version of BirdNET-PI at https://github.com/Nachtzuster/BirdNET-Pi[github.com/Nachtzuster/BirdNET-Pi]
+
Learn more at: https://birdnetpi.com[BirdNETPi.com]
| image:https://tuc.cloud/index.php/s/jDtyG9W36WwKpbR/download/logo_box_birdweather.png[BirdWeather,300,link=https://app.birdweather.com]
@@ -188,6 +155,12 @@ Using the BirdNET artificial neural network, BirdWeather is continuously listeni
Learn more at: https://app.birdweather.com[BirdWeather.com]
+| image:https://tuc.cloud/index.php/s/kqT7GXXzfDs3NyA/download/birdnetlib-logo.png[birdnetlib,300,link=https://joeweiss.github.io/birdnetlib/]
+| *birdnetlib* +
+A python api for BirdNET-Analyzer and BirdNET-Lite. `birdnetlib` provides a common interface for BirdNET-Analyzer and BirdNET-Lite.
+
+Learn more at: https://joeweiss.github.io/birdnetlib/[github.io/birdnetlib]
+
| image:https://tuc.cloud/index.php/s/zpNkXJq7je3BKNE/download/logo_box_ecopi_bird.png[ecoPI:Bird,300,link=https://oekofor.netlify.app/en/portfolio/ecopi-bird_en/]
| *ecoPi:Bird* +
The ecoPi:Bird is a device for automated acoustic recordings of bird songs and calls, with a self-sufficient power supply.
@@ -241,6 +214,41 @@ We have created an interactive map of projects that use BirdNET. If you are work
You can access the map here: https://kahst.github.io/BirdNET-Analyzer/projects.html[Open projects map]
+== Model version update
+
+[discrete]
+==== V2.4, June 2023
+
+* more than 6,000 species worldwide
+* covers frequencies from 0 Hz to 15 kHz with two-channel spectrogram (one for low and one for high frequencies)
+* 0.826 GFLOPs, 50.5 MB as FP32
+* enhanced and optimized metadata model
+* global selection of species (birds and non-birds) with 6,522 classes (incl. 10 non-event classes)
+
+You can find a list of previous versions here: https://github.com/kahst/BirdNET-Analyzer/tree/main/checkpoints[BirdNET-Analyzer Model Version History]
+
+[discrete]
+==== Species range model V2.4 - V2, Jan 2024
+
+* updated species range model based on eBird data
+* more accurate (spatial) species range prediction
+* slightly increased long-tail distribution in the temporal resolution
+* see https://github.com/kahst/BirdNET-Analyzer/discussions/234[this discussion post] for more details
+
+== Technical Details
+
+Model V2.4 uses the following settings:
+
+* 48 kHz sampling rate (we up- and downsample automatically and can deal with artifacts from lower sampling rates)
+* we compute 2 mel spectrograms as input for the convolutional neural network:
+** first one has fmin = 0 Hz and fmax = 3000; nfft = 2048; hop size = 278; 96 mel bins
+** second one has fmin = 500 Hz and fmax = 15 kHz; nfft = 1024; hop size = 280; 96 mel bins
+* both spectrograms have a final resolution of 96x511 pixels
+* raw audio will be normalized between -1 and 1 before spectrogram conversion
+* we use non-linear magnitude scaling as mentioned in http://ceur-ws.org/Vol-2125/paper_181.pdf[Schlüter 2018]
+* V2.4 uses an EfficienNetB0-like backbone with a final embedding size of 1024
+* See https://github.com/kahst/BirdNET-Analyzer/issues/177#issuecomment-1772538736[this comment] for more details
+
== Setup
=== Setup (Raven Pro)
@@ -252,14 +260,21 @@ https://tuc.cloud/index.php/s/2TX59Qda2X92Ppr/download/BirdNET_GLOBAL_6K_V2.4_Mo
=== Setup (Python package)
-The easiest way to setup BirdNET on your machine is to install https://pypi.org/project/birdnet/[birdnet] through pip with:
+The easiest way to setup BirdNET on your machine is to install https://joeweiss.github.io/birdnetlib/[birdnetlib] or https://pypi.org/project/birdnet/[birdnet] through pip with:
[source,sh]
----
+
+pip3 install birdnetlib
+
+or
+
pip3 install birdnet
----
-You can run BirdNET with:
+Please take a look at the https://joeweiss.github.io/birdnetlib/#using-birdnet-analyzer[birdnetlib user guide] on how to analyze audio with `birdnetlib`.
+
+When using the `birdnet`-package, you can run BirdNET with:
[source,python]
----
@@ -283,7 +298,7 @@ print(f"predicted '{prediction}' with a confidence of {confidence:.6f}")
----
For more examples and documentation, make sure to visit https://pypi.org/project/birdnet/[pypi.org/project/birdnet/].
-For any feature request or questions regarding *birdnet*, please add an issue or PR at https://github.com/birdnet-team/birdnet[github.com/birdnet-team/birdnet].
+For any feature request or questions regarding `birdnet`, please add an issue or PR at https://github.com/birdnet-team/birdnet[github.com/birdnet-team/birdnet].
=== Setup (Ubuntu)
diff --git a/docs/assets/js/projects_data.js b/docs/assets/js/projects_data.js
index ee7bebee..39db3e4e 100644
--- a/docs/assets/js/projects_data.js
+++ b/docs/assets/js/projects_data.js
@@ -1040,6 +1040,36 @@ var projects_data = [
"Species Image": "https://upload.wikimedia.org/wikipedia/commons/4/46/Barred_Owl_%28Strix_varia%29_RWD.jpg",
"Species Image Credit": "Dick Daniels, via Wikimedia Commons",
"Species Icon": "feather"
+},
+{
+ "Project name": "Coffee production bird monitoring",
+ "Organization/Project lead": "Marius Somveille, University College London",
+ "Target species": "Birds",
+ "Country": "Mexico",
+ "Region/Location": "Soconusco Region, Chiapas",
+ "Latitude": 15.31,
+ "Longitude": -92.72,
+ "Contact": "marius@somveille.com",
+ "Website": null,
+ "Paper": "https://www.biorxiv.org/content/10.1101/2024.07.12.603271v1",
+ "Species Image": "https://upload.wikimedia.org/wikipedia/commons/5/5c/Collared_Aracari_%2844517373101%29.jpg",
+ "Species Image Credit": "Andy Morffew from Itchen Abbas, via Wikimedia Commons",
+ "Species Icon": "feather"
+},
+{
+ "Project name": "Common cuckoo vocalisations as an indicator",
+ "Organization/Project lead": "Adam Mickiewicz University in Poznan, Poznan, Poland",
+ "Target species": "Common cuckoo",
+ "Country": "Poland",
+ "Region/Location": "Odolanow, Poland",
+ "Latitude": 51.575,
+ "Longitude": 17.676,
+ "Contact": "dmwiniarska1@gmail.com (D. Winiarska)",
+ "Website": null,
+ "Paper": "https://www.sciencedirect.com/science/article/pii/S1470160X24008574",
+ "Species Image": "https://upload.wikimedia.org/wikipedia/commons/4/44/Common_Cuckoo_%28Cuculus_canorus%29_%288079424957%29_%282%29.jpg",
+ "Species Image Credit": "Ron Knight, via Wikimedia Commons",
+ "Species Icon": "feather"
}
];
diff --git a/docs/projects.html b/docs/projects.html
index 50e091b6..5a9ed402 100644
--- a/docs/projects.html
+++ b/docs/projects.html
@@ -45,7 +45,7 @@ projects worldwide