First you deploy this technology to the areas that you know have been experiencing gunfire. Several microphones are used and when gunfire is “heard” by the server, it can be triangulated to the source location. If you have a video network, you can also move to the source. Guess the server program is not identifying the gunshot frequency correctly.
There may be a typical distinctive range but it isn’t a range unique to firearms. There’s also real world variables at play here such as gunshots outdoors vs indoors that’d force them to broaden that range leading to more false positives.
Take an o-scope and a digital microphone setup, fire a gun, and i promise you you’ll not see the same reading with anything else, But, if you’re tied to an argument go ahead and do it with someone else.
Way too many variables. Are you shooting 9mm, 45, .338 Lapua, .22lr? Are you shooting from a 9 in barrel, a 16 in, a three inch? Are you using a muffler? What brand and type? What is your range to the gun shot? Is the bullet supersonic or subsonic? Does the roofers hammer make a sound that falls inside the wide range of noises? There is no way to make an accurate profile for gun shot noise.
Well when I ran destructive test we used an assortment of guns. I suppose to collect this data you would do the same thing and use the average sample across the timeline for your programs baseline. Just a guess.
First you deploy this technology to the areas that you know have been experiencing gunfire. Several microphones are used and when gunfire is “heard” by the server, it can be triangulated to the source location. If you have a video network, you can also move to the source. Guess the server program is not identifying the gunshot frequency correctly.
So we need to know both the range and the false positive rate, as well as the response
I’d imagine there’s a higher prevalence of gunshots in the non-white areas so more mics are placed there.
Part of the problem is that there’s other loud noises that sound like gunshots like a car backfiring.
The way I understand it, gunfire has a distinctive frequency range that can be measured.
There may be a typical distinctive range but it isn’t a range unique to firearms. There’s also real world variables at play here such as gunshots outdoors vs indoors that’d force them to broaden that range leading to more false positives.
Take an o-scope and a digital microphone setup, fire a gun, and i promise you you’ll not see the same reading with anything else, But, if you’re tied to an argument go ahead and do it with someone else.
Way too many variables. Are you shooting 9mm, 45, .338 Lapua, .22lr? Are you shooting from a 9 in barrel, a 16 in, a three inch? Are you using a muffler? What brand and type? What is your range to the gun shot? Is the bullet supersonic or subsonic? Does the roofers hammer make a sound that falls inside the wide range of noises? There is no way to make an accurate profile for gun shot noise.
Well when I ran destructive test we used an assortment of guns. I suppose to collect this data you would do the same thing and use the average sample across the timeline for your programs baseline. Just a guess.