sbts-aru

I developed a recorder project last year that make's this easier and you could deploy it on a Raspberry Pi zero and use a wifi link. https://github.com/hcfman/sbts-aru It's using a cheap (0.40c) sound card, but personally I choose to have high quality mics (primo em272 mic capsules, used in high quality parabolic systems. However, there's nothing to stop you using < 10.00 euro microphones.

If you want to setup a chrony time server that maintains accuracy to within a microsecond and doesn’t do this with a network connection then you could try my sbts-aru project and just not use the audio recorder parts of it. https://github.com/hcfman/sbts-aru It installs with a single command on all Raspberry Pi versions and takes care of all the dependencies, configuration and startup order details to install and start working with one command. It’s a sound localizing audio recorder platform and that’s why it also sets up accurate time. It’s using GPS to get its time from.

Real nice resource! And if any geeks want to get involved with adding to the recordings but don’t want to invest much, I have a raspberry pi recorder project that can help. It even runs on a pi zero. https://github.com/hcfman/sbts-aru As an added bonus, if what you are recording can be heard on three recorders or more you can sound localize with them. Find out where that wood pecker is pecking from for example. Lastly. You can synchronise the time on all your computers with it as it doubles as a time server with sub-microsecond time accuracy. (PS. Love the comments about New Zealand here, I’m from New Zealand :-) )

My project, sbts-aru, https://github.com/hcfman/sbts-aru uses a GPS to synchronise the time with and then even when running completely disconnected from any network the clocks will be accurate to real time with less than 1 microsecond of error. Typically the system time hovers around 100 ns or less from the real time. And I’ve tested this by triggering interrupts on gpio’s on two devices with the same switch and printing the time. Syncing to sub microsecond accuracy is usually achieved in under a minute.

That’s a nice read, thank you for that. If he sets up three or more recorders then next time he can find out within minutes of the first bang to within meters. But his article presents a fun challenge. He doesn’t say where his receiver locations are but he does give a triangle dimensions. I wonder if it’s possible to map this onto the street names and then work out which farm it was. It seems likely that there is only one way to map that triangle so each vertex is on the street it is reported to be on. Then you could invent a time, any time and add the number of seconds that he mentions to get the two other times. Now you have the times and the co-ordinates, within the constraints from the time inaccuracies you could run the docker version of my localization program (https://github.com/hcfman/sbts-aru) on this input and it would output th

Nice to see people able to use tech to help reduce/manage their stress/trauma in such horrific situations. Good point about telegram. As much local control as possible is desirable. Do the text to speech interfaces work offline with the chosen devices ? If so, I’ll likely have a play. I have a project that might be able to help with your situation. A Raspberry Pi based sound localization system. It’s very accurate. Last weekend I localized an explosion (fireworks) to within 20m from the actual location with 4 recorders. two of which were 3km from each other. Unlike most ARUs (autonomous recording units) which are based on microcontrollers and need post processing to determine an event start time, the Pi system could be used as the basis for a real time localization system as the system times is sub microsecond accurate. With likely a smal

Nice article and lovely piece of kit that one from sparkfun. Will likely be getting one next year to further my experiments with gunshot localization, on my Pi-based sound localization platform (https://github.com/hcfman/sbts-aru). Though at the distances I'm localizing the phone based GNSS seems to be working quite well. With an RTk GNSS though I could do some experiments with localizing bat calls. I've tested the Pettersson Ultrasonic microphones with my localization platform and that works fine. I suspect that the bat localization I could achieve if my co-ordinates had RTK accuracy would be pretty damned good! But I have to try it to know.

Unfortunately a Raspberry Pi is a bit ill suited for production environments. Id recommend an RTC module. Otherwise this might be helpful: https://github.com/hcfman/sbts-aru

Well... I see where you are going with this but is it necessary? What happens if the clock gets it's correct time sync back, will not wireguard come back online by itself ? Then the real solution might involve ensuring good clock sync. Using a GPS synched time source is good for this, such as this one :) https://github.com/hcfman/sbts-aru

Install sbts-aru as a base system and it will keep your Pi accuracy to sub-microsecond error. https://github.com/hcfman/sbts-aru Problem solved.

If you install my sbts-aru project https://github.com/hcfman/sbts-aru It will shrink your partitions, add new ones and install one of these and set up a sub micro second system clock and an audio recorder suitable for sound localization with a single install command. Works on all versions of Pi in both raspbian and bookworm. If you don’t need the recorder just turn it off but you have a rootfs, a swap fs, a config fs and a disk fs.

Yep. This project https://github.com/hcfman/sbts-aru

I recommend primo EM272 microphone capsules for use with https://github.com/hcfman/sbts-aru. They are high quality, very sensitive with high signal to noise ratio, lauded for nature recording use cases. They can be bought assembled for around 65 euros in the Netherlands. However these capsules are often found in much more expensive equipment.

Which is why we need public counter validation. (https://github.com/hcfman/sbts-aru) :-)

Categorisation will certainly be error prone as there is so much similarity because fireworks and gunshots. The main difference I expect is the speed of the shockwave resulting in steeper spectrograms for gunshots. However, a steep spectrogram is essentially showing a large amount of high as well as low frequencies. The higher frequencies degrade very quickly over distance reducing the difference of a gunshot from a firework the more this degrades. Not finding evidence is a different problem, it could be as simple as using a revolver rather than a semi-auto that ejects the cartridges. In any case, it would be great to see independent testing of this problem by the sort of people in this forum and you can use the software I developed last year (https://github.com/hcfman/sbts-aru) to do so. That software sets the time on the Raspberry Pi t