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.
Installation
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 https://github.com/kahst/BirdNET-Analyzer.git
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 analyze.py 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 analyze.py --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.