Processing MicroMoth recordings offline

The uMoth generates .wav files, uncompressed waveforms of what it records. These need to be processed to identify any bird calls within them.

This function is integrated in BirdNET-Pi, which does recording and classification, and provides a web GUI. With the uMoths we need to provide classification as part of a data processing pipeline. We can however make direct use of the classifier “brain” within BirdNET-PI, which is unsurprisingly called BirdNET-Analyzer.


I’m working on a 16-core Intel Core i7@3.8GHz running Arch Linux.

First we clone the BirdNET-Analyzer repo. This takes a long time as it includes the ML models, some of which are 40MB or more.

    git clone
    cd BirdNET-Analyzer

The repo includes a Docker file that we can use to build the analyser in a container.

    docker build .

The container setup is quite basic and is probably intended for testing rather than production, but it gives a usable system that could then be embedded into something more usable. The core of the system is the script.

Analysing some data (AKA identifying some birds!)

The container as defined looks into its /example directory for waveforms and analyses them, generating text file for each sample. The easiest way to get it to analyse captured data is to mount a data directory of files onto this mount point (thereby shadowing the example waveform provided).

There are various parameters that configure the classifier. I copied the defaults I was using with BirdNET-Pi, only accepting classifications at or above 0.7 confidence.

    docker run -v /var/run/media/sd80/DATA:/example birdnet-analyzer --rtype=csv --min_conf=0.7 --sensitivity=1.25

This crunches through all the files (982 of them from my first run) and generates a CSV file for each. An example is:

Start (s) End (s) Scientific name Common name Confidence
6.0 9.0 Corvus monedula Eurasian Jackdaw 0.9360
9.0 12.0 Corvus monedula Eurasian Jackdaw 0.8472
12.0 15.0 Corvus monedula Eurasian Jackdaw 0.8681
15.0 18.0 Corvus monedula Eurasian Jackdaw 0.8677
24.0 27.0 Columba palumbus Common Wood-Pigeon 0.9198
27.0 30.0 Columba palumbus Common Wood-Pigeon 0.7716
45.0 48.0 Corvus monedula Eurasian Jackdaw 0.8023
48.0 51.0 Corvus monedula Eurasian Jackdaw 0.7696

Those are entirely credible identifications. The start- and end-point offsets allow rough location within the recording. (BirdNET segments the recordings into 3s chunks for analysis.)

This is clearly not as straightforward as BirdNET-Pi, nor as immediately satisfying. But it does scale to analysing lots of data (and could be made to do so even better, with a better front-end to the container), which is important for any large-scale deployment.

(Part of the series Bird counting with acoustic sensing.)