A Road to Common Lisp

This a really brief, yet really interesting, approach to introducing Lisp to someone. Interesting because it covers all the usual ground, but also has copious pointers to other material slightly-beyond-introductory (“Where to go from here”). It also links to material that’s essential to modern practice, such as Lisp packages and systems, and the essential “standard libraries” such as Alexandria, Bordeaux, CL-PPCRE, usocket, and the like: the things that are needed in practice and which in other languages would probably be built-in and included directly in an introduction.

The BirdNET-Pi system aims to provide out-of-the-box bird identification. It’s slightly more awkward than that, but still pretty straightforward to get up and running.

My first hardware plan was to use a Raspberry Pi Zero as the compute host with a Waveshare WM8960 HAT for the sound capture. It turns out that BirdNET needs a 64-bit platform – why I’m not sure – and the Pi Zero only runs 32-bit Linux. I therefore moved to a Raspberry Pi B that I had lying around, and put a 64-bit “lite” install on it to run headless.

I then basically just followed the installation guide. There was an issue with the installation script when cloning the GitHub repo: I suspect this was because of limited memory on the Pi. I downloaded manually, and manually ran the rest of the install script, which did a lot of setup of services and a PHP web server.

I compiled the drivers for the HAT, which worked fine. The new sound card is recognised but is not the system default.

The installed components seem to include:

• icecast2, a streaming server, used to replay recordings
• caddy web server
• PHP for serving the web pages
• arecord to actually record audio
• ffmpeg to extract waveforms
• sqlite for the database
• the actual machine learning model used for recognition

The recognition models are built with TensorFlow. This is a great example of how the standard Linux tools and services can be combined to get a scientific-grade sensor platform. (Caddy doesn’t seem to be running over TLS by default, which would be an issue outside a firewall.)

Since the sound card isn’t the default, the easiest way to get the system listening to the right mics is to change the device in the “advanced settings” panel: in my case I changed from “default” to “hw:2,0”, reflecting the output of arecord -l that shows the sound card devices.

I then deployed the Pi out of the kitchen window.

To start with it wasn’t hearing anything, which I think may be because of the waterfall in the courtyard: turning this off made things much more effective:

That’s an appropriate set of birds being seen – and we hardly ever see magpies, but know they’re around. There’s actually quite a lot of background noise even in such a quiet village, but the bird calls do stand out.

I can’t see any reason for the manual installation on bare metal: as far as I can see everything could be containerised, which would make deployment and management a lot easier.

Downloads of epydemic, my library for epidemic (and other) process simulation on networks (and hopefully other combinatorial structures soon…) recently passed 100,000.

That number comes from the project’s PePy page, which tracks downloads from the main PyPi page as used by pip. I can’t say whether or not that number is accurate. Quite honestly it’s at least 98,000 more than I ever expected, but 100,000 feels like something of a milestone to be pleased about.

epydemic came about because of a lack of standard tooling for doing epidemic simulation. This involves a lot of stochastic simulation, which is quite tricky code to write and to make efficient. Testing the system actually involved us thinking more deeply about the effectiveness of unit testing for stochastic code, which then led to a presentation at UK Systems in 2021 explaining the problems we’d had.