Hi all, Nice group! I'm building an acoustic monitoring device that I wanted to share as it may be of interest to some people and also I'd like to know of any requirements that people in bioacoustic research would like in such a device.
Currently I have the following working:
* Raspberry pi device with software that is recording from a USB microphone (Currently cheap one, with mic replaced with em172 capsule).
* Time synchonization with GPS and PPS, system time is around 1us accuracy, typically lower
* Multi-thread C program that records from this source storing the files in a date based structure marking the start and end date/times to microsecond accuracy. The start time should be extremely accurately record, I'm expecting sub 1/2ms but still have to test and confirm this.
* Used defined file record length period. I'm currently using 10 mins because each new time in re-aligns the time to u-sec accurate time.
* Have intentions to connect an i2c thermometer and then also include the start of file temperature.
* File rotation program to compress to flac (Or if useful, other formats)
I've started with a cirrus-logic cm108 based USB recorder with em172 capsules because of historical reasons. But I've just ordered some Seeed Studios 2 mic pi hats that I intend to add to the system.
In principle, I'm expecting to be able to build a recorder system with extremely accurate GPS based timing for less than 100 euros that can record at sample rates up to 48000 Hz that should be ideally suited for sound localization. All of the components should be off the shelf and easily available (Caveat, pi's are currently more difficult but that is expected to ease in July). In any case, the microphone hat should be very readily obtainable.
Let me know if this is of interest to anyone and if you have any special desires or requirements you would like to have in the system above what I've mentioned above.
PS. I also have another very mature project (More than 10 years old that would be really suited for poacher or animal detection and alerting using the most advanced computer vision objection detection algorithms available (yolov6 and yolov6). But that's for another discussion.