BirdPROBE

BirdPROBE does offline live monitoring of bird vocals using the BirdNET artificial neural network.

Design

BirdPROBE consists of several components loosley connected through a MQTT broker:

• birdprobe-detection - records audio and does detection based on BirdNET, publishes it's detections via MQTT
• birdprobe-display - displays detections and status informations via external displays by listening to MQTT events
• birdprobe-location - publishes location information via MQTT from different providers (none, static, gpsd)
• birdprobe-record - records detection events TODO
• birdprobe-sysclock - approximate system time from gpsd for budget gps dongles (for SBCs w/o RTC like Raspberry Pi)

All components can run on a single host. They also can run distributed or you could run multiple instances. By default the MQTT topics contains the $(hostname) string to distinguish component instances per node.

Status

This is project is in WIP and not ready to use, yet.

Usage

MQTT Broker

You need a MQTT broker. You can use a local mosquitto:

# apt-get install mosquitto mosquitto-clients

Install

Install external dependencies for birdnetlib and pyaudio python packages:

# apt-get install ffmpeg virtualenv portaudio19-dev

Clone this repository locally:

$ git clone https://codeberg.org/liske/BirdPROBE.git

Create a python3 virtualenv

$ cd BirdPROBE
$ virtualenv -p python3 venv
$ . venv/bin/activate

Install BirdPROBE:

$ pip install -e .

You can add optional features:

• gps - gpsd based location provider
• ws-epd- waveshare-epaper display support

$ pip install -e '.[gps,ws-epd]'

Run

The components provide appropriate commands in the virtualenv to start them.

Run the detection component:

$ birdprobe-detector -c /path/to/config.ini

Run the location provider:

$ birdprobe-location -c /path/to/config.ini

Monitor MQTT messages:

$ mosquitto_sub -v -t 'BirdPROBE/#'

TODO

• provide some packaging
• finish components
• logging
• more optimization for low power setups
  • use some binary rather than JSON message format
  • update prediction only if location has significant changes
  • update week_48 less often