klettermann
Senior Member
There's not much point here except to share the fun. I thought I'd use ChatGBT as an audio analysis supercomputer to work on room reflection issues and it worked out very nicely. 
Here goes.
I started by providing a room layout and impulse responses from the main speakers which were already EQd and XOd with 2 subs. Subs were muted. Room is shown below. Speakers are large Maggies and the scale is 6"/line. I let ChatGPT figure out all the dimensions from the grid. Left and right impulse traces are below. ChatGPT evaluated the impulses and did the math to figure out the distances which correlated quite exactly with the path lengths show in the room picture. The cavity on the left is a solid outside door. Path C2 bounces off a large column.
Left Right
Here's ChatGPT's summary:
"
f ≈ 1 / (2 × delay)
So:
2.2 ms → ~227 Hz fundamental comb spacing
3 ms → ~167 Hz spacing
6 ms → ~83 Hz spacing
But these reflections affect wide bands, not single frequencies.
The 2–3 ms region is especially audible for imaging precision.
What This Means
Confirmed mapping:
LEFT
Priority Ranking for Treatment
So.... I built a couple panels about 6'x2' with 67" rockwool and remeasured. The results, I think, were pretty good. Below are combined L+R sweeps before and after.
L+R, untreated
L+R, Panels in place
What were the actual sonic results? Actually pretty damn good IMO. The main result was improved vocal clarity and greatly improved positioning/location within the soundstage. Both were already pretty good, so I wasn't feeling driven to "fix" anything. But this really did bump it up significantly. What's next? I'll build a couple more panels and just play with them. As for ChatGPT, it save me a lot of arithmetic and worked as kind of gentle coach. I thought it was pretty cool that the whole analysis actually matched up with some measurable changes that are actually audible. Fun stuff. Cheers,
Here goes.I started by providing a room layout and impulse responses from the main speakers which were already EQd and XOd with 2 subs. Subs were muted. Room is shown below. Speakers are large Maggies and the scale is 6"/line. I let ChatGPT figure out all the dimensions from the grid. Left and right impulse traces are below. ChatGPT evaluated the impulses and did the math to figure out the distances which correlated quite exactly with the path lengths show in the room picture. The cavity on the left is a solid outside door. Path C2 bounces off a large column.
Left Right
Here's ChatGPT's summary:
"
Frequency Correspondence
Comb cancellation frequency roughly:f ≈ 1 / (2 × delay)
So:
2.2 ms → ~227 Hz fundamental comb spacing
3 ms → ~167 Hz spacing
6 ms → ~83 Hz spacing
But these reflections affect wide bands, not single frequencies.
The 2–3 ms region is especially audible for imaging precision.
What This Means
Confirmed mapping:
LEFT
- 3 ms → D1 (side wall)
- 6 ms → D2 (longer wall path)
- 2.2 ms → C2 (column)
- 6 ms → C3 (side wall)
- 8–9 ms → C1 (cross room)
Priority Ranking for Treatment
- Column face (C2) — highest priority
- Left side wall first reflection (D1)
- Right side wall first reflection (C3)
- Everything >8 ms — lower priority
So.... I built a couple panels about 6'x2' with 67" rockwool and remeasured. The results, I think, were pretty good. Below are combined L+R sweeps before and after.
L+R, untreated
L+R, Panels in place
What were the actual sonic results? Actually pretty damn good IMO. The main result was improved vocal clarity and greatly improved positioning/location within the soundstage. Both were already pretty good, so I wasn't feeling driven to "fix" anything. But this really did bump it up significantly. What's next? I'll build a couple more panels and just play with them. As for ChatGPT, it save me a lot of arithmetic and worked as kind of gentle coach. I thought it was pretty cool that the whole analysis actually matched up with some measurable changes that are actually audible. Fun stuff. Cheers,
