MXXX1: Trouble cloning TurboReveb module results by capturing IR and using in Convolution module

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In theory, a reverb produced by TurboReverb module with no modulation or dynamics should be able to be perfectly reproduced by burning that IR and using it in Convolution module, right? So why isn't this working?

In MXXX1 preset below, TurboReverb module has a single "R" algorithm in a single Late Reflections generator with no dynamics or modulation, and the Convolution module is set to just play the unstretched IR with no modifications or effects added.

Since preset doesn't include IR, and TReverb module lacks mTurboReverb's "IR" button to generate one, patch from the TReverb module needs to be copied and pasted into MTR plugin to generate the IR.

If you do that, and load the IR into the Convolution module, then A/B them by alternating which lane is muted, you can hear clear differences. Why? AFAIK, I've eliminated anything like modulation or dynamics, and left only what should be able to be captured perfectly.

First, the levels don't match. How do I get an exact level match between the original algorithmic reverb and the Convolution IR recreation such that it consistently matches each time I redo this process with a new algorithm?

Second, there are sonic differences even after the results are level-matched. Do "r" or some of the algos I've been trying have some sort of modulation going on I'm not aware of? Is there something else that can't be properly captured? Some setting i've missed on the convolution side that's messing up the use of the IR?

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I tried and here it sounds virtually the same. BUT notice the limiter you have enabled and also to avoid loudness changing, change the loudness adjustment in convolution's advanced settings.
Vojtech
MeldaProduction MSoundFactory MDrummer MCompleteBundle The best plugins in the world :D

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I've been doing this a bunch, and found a few patches since where the difference is considerably more noticeable. If its an issue again, I'l provide a more extreme example. I was under the impression the limiter is only a hard clip of the output above 0dB, so shouldn't affect unless there's a SUPER loud input. Have I misunderstood something else the limiter is doing that would affect the IR?

I have been adjusting the convolution loudness adjustment, but what I find odd (and makes me think I'm doing something wrong) is that it needs a DIFFERENT adjustment for each verb I capture... so it seems there is something else that is affecting volume besides some static mismatch between TurboReverb and Convolution modules. Not a huge deal, I was just concerned that whatever is causing that inconsistent volume may also be causing other inconsistencies when the whole purpose of my building current device is to streamline making carbon copy clones of MTR for use in mConvolution.

Some bugs to work out, but it's a SUPER useful device so far, btw. So much so it's a head scratcher as to why reverbs don't exist that let you just design something, then capture and replay the IR within the same plugin. My CPU went from being pegged and constant error messages to no core over 50% and MOST of them sound very close.

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I think it needs to be about the IR. But another part of the gain staging may be the problem too, hard to say.

Anyways be careful with convolution. I assume you are designing some very very complex reverbs, in which case this perhaps can be useful. But note that with convolution the plugin is actually creating multiple threads to work on the convolution in the background. That's quite OK these days when the CPU's aren't getting much faster and instead they are getting more cores. But it's needed to understand, that convolution actually requires hell of a lot of processing, but it is masked by the fact that most of it processed in the background and not measured by the DAW's meters.
Vojtech
MeldaProduction MSoundFactory MDrummer MCompleteBundle The best plugins in the world :D

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