Reverse Engineering a Spatial Audio Effect
Learn how to reverse engineer a commercial spatial audio effect with impulse responses, sweeps, and Linux convolution—and see where a fixed filter falls short.
How to Rebuild a Spatial Audio Effect by Measuring It
A commercial audio effect can sound simple from the outside: turn it on, and the music feels less trapped inside your head. Recreating that feeling is harder. You can fiddle with familiar filters, experiment with head-related transfer functions, or follow plausible explanations of psychoacoustic techniques. But if you can't compare your result against the original, you're mostly guessing.
I went a different way. Instead of trying to reverse-engineer the commercial processor's internal algorithm, I measured what it did to audio and tested whether a fixed convolution filter could reproduce the output. That reframed everything into questions I could actually check: Is the processor linear? Does its behavior drift over time or change with input level? Can a stereo effect be captured with four impulse responses? And - most importantly - how close does the resulting filter get on audio it wasn't fitted against?
This article walks through the measurement process, the assumptions behind it, and what the results turned out to show. The practical lesson is straightforward: you don't have to know how an effect is built to reproduce it, but you do need to figure out which parts of its behavior are stable enough to model.
Why I stopped trying to guess the algorithm
I was trying to recreate an effect I'd used on another operating system. It made music sound farther from my ears and less "on the ear," while keeping the musical details clear. After listening longer, it also felt less blocked. I wanted something similarly simple to use on Linux.
My first attempt was the usual path: build it from familiar audio-processing ideas. Since I was targeting Linux, PipeWire was the starting point. I'm not an audio engineer, so I spent time experimenting with standard filters and psychoacoustic techniques, and I used an LLM as a sounding board along the way.
That led me to head-related transfer functions - HRTFs. The basic idea is that sound from a particular direction gets shaped by the listener's head and ears. I started with the MIT KEMAR HRTF database, but the anechoic recordings were missing the room response I was looking for. To me, it sounded artificial and too much "inside my head." The SADIE II database, which includes room responses, matched the target more closely.
It was progress, but it wasn't a method I could trust. Terms like "perceptual envelopment" and "late-field decorrelation" gave me possible concepts, not a clear checklist for what to measure or tweak. I could tell when something sounded off, but the vocabulary didn't tell me what to do next.
Measure behavior, not implementation
The change that mattered was refusing to ask, "What algorithm does this program use?" and instead asking, "What output does it produce for a known input?" That question is easier to answer.
In real reverse engineering, you often don't get access to the internals. I could only run the original program on a friend's laptop, so repeatedly changing my code and immediately comparing to the reference wasn't practical. What I could do was generate a test file on Linux, run it through the commercial processor, and record the result. One recording session could then feed multiple analyses.
This is the heart of it: you don't need the internal implementation if your model reproduces the output well enough for your use. And conversely, even a plausible explanation of the algorithm is pointless if it doesn't predict what the processor actually does.
How I designed a useful measurement file
My first question was whether the processor behaved like a linear time-invariant (LTI) system. If it does, then the impulse response captures everything we need. In that case, any input can be treated as a sum of scaled and shifted impulses, and the output becomes a corresponding sum of scaled and shifted responses.
For a single-channel system, that's convolution:
y[n] = ∑k x[k]h[n − k] = (x * h)[n]
Here, x is the input, h is the impulse response, and y is the output. For a stereo processor, you need four impulse responses, because each input channel can affect both output channels:
[yL, yR]T = [[hLL, hLR], [hRL, hRR]] * [xL, xR]T
The subscripts are the "route": for example, hRL is the response at the right output to a signal sent into the left input.
Test the assumptions behind convolution
A single impulse is a good starting point. Put one nonzero sample into an otherwise silent file, run it through the processor, and record the output. Everything the effect does after that sample - direct sound, cross-channel leakage, phase shifts, and decay - shows up in the response.
But you only get a valid impulse response if the processor behaves consistently. So I tested the assumptions directly:
- Linearity: A louder input should produce the same response at a proportionally higher level, so F(ax) = aF(x).
- Additivity: Two inputs played together should produce the sum of the separate outputs, so F(x1 + x2) = F(x1) + F(x2).
- Time invariance: The same input played later should produce the same response, just shifted in time.
The test file included impulses at different amplitudes, impulses in each channel by itself, and impulses played in both channels together. I also inverted one channel in a paired test. If the system is linear, the combined responses should add in the first case and subtract in the second. I left enough silence between tests for each response to decay before the next one began.
These aren't academic details. If the processor changes gain based on input level, one impulse response can't cover every situation. If it changes behavior over time, a filter measured at one moment won't reliably predict what happens later.
Use sweeps and tones to expose other behavior
An impulse contains energy across the frequency range, but a one-sample impulse gives each frequency almost nothing to work with. That makes the measured response more sensitive to noise. A sine sweep spends more time across frequencies, so it tends to give a more reliable estimate when the input has enough energy. I used the sweep as a second measurement and compared it to the impulse-based estimate. The sweep method is also discussed in this swept-sine paper by Angelo Farina.
In the frequency domain, a basic estimate divides the output spectrum by the input spectrum. If the input has very little energy at some frequencies, that division can blow up recording noise. A regularized estimate avoids that:
Ĥ(f) = [Y(f)X*(f)] / [|X(f)|2 + λ]
This complex-valued estimate keeps phase information, which matters for stereo. A signal arriving at the opposite channel with a delay isn't captured well by magnitude alone. The regularization term λ prevents unstable estimates where the input doesn't provide enough information.
I also included steady tones at different frequencies and levels, longer tones that ramped up and down, and a multitone signal. Those tests helped separate different behaviors: level-dependent gain, how quickly gain changes settle, and whether processing introduces new frequency content. Because I repeated sections, I could check whether the processor produced the same output for the same input each time.
Finally, I kept a noise section separate from the measurement signals. That gave me a practical validation: could the filter extracted from the test signals reproduce the processor's output on audio it hadn't been fitted to directly?
How to recover and validate the filter
I started playback and recording manually, so the captured file didn't begin at exactly the same point as my generated test file. Before comparing signals, I estimated the recording offset.
I placed evenly spaced clicks near the start of the probe. My first idea was to correlate the probe clicks directly with the recorded clicks. It was less reliable than I expected, because the processor can spread each click over time. Instead, I computed local energy by summing squared samples over a window, then searched for the offset that produced the strongest match:
τ̂ = arg maxτ ∑n eprobe[n] erecording[n + τ]
I used the same offset for both recorded channels. If I aligned them independently, I could accidentally erase or distort the left/right timing difference that's part of what we're trying to measure.
Compare like with like
Once the signals were aligned, I compared the original recording with the output from the extracted filter. A simple relative error measure is:
ε = ‖y − ŷ‖2 / ‖y‖2
Here, y is the recorded output and ŷ is what the extracted response produces when convolving with the test input. Lower error is useful, but the number doesn't mean much on its own. Repeated sections already differed slightly, so expecting perfect identity is unrealistic.
So I compared the model's error to the error between repeated recordings. If the model error is close to the repeat-to-repeat variation, then the model is doing about as well as the measurement setup allows:
εmodel ≈ εrepeat ≪ 1
This is a more honest standard than demanding zero error. With a physical recording setup, playback, capture, and the reference processor can all introduce small differences. The real question is whether the model misses meaningful behavior beyond that baseline.
Choose filter length using held-out audio
Measured responses can run for a long time. Truncating the response makes the filter cheaper to run, but it also discards part of the effect's decay. Keeping everything preserves more detail, but increases cost - and it might be unnecessary if the late tail contributes little.
To pick a length, I tested increasingly long filters against the reserved noise section. For each length N, I measured the relative prediction error:
εN = ‖ynoise − h(N) * xnoise‖2 / ‖ynoise‖2
The full response predicted the recorded noise better than the shorter versions, so I kept it. This ties the decision to how well the filter predicts separate audio, rather than relying only on visual inspection.
The same idea applies more broadly: measure using known probes, then validate using material you didn't use to derive the model. A filter that only matches the measurement file might be overfitting the test setup - or it might miss parts of the behavior that matter for everyday audio.
What the measurements revealed
For the processor's main surround setting, the behavior lined up closely enough with an LTI system that a fixed stereo convolution filter captured it well. The scaled impulses were nearly identical, paired inputs matched the sum of their separate responses, and the multitone produced almost no new frequency content. Repeated sections were also consistent enough that the model's error could be compared meaningfully to recording variation.
Those four responses tell you what the effect is doing in practical terms. When sound is sent to one side, it also shows up in the opposite channel - quieter and slightly delayed - followed by a long tail. Those pieces add up to the out-of-head quality I was trying to build manually with HRTFs and room responses.
That said, not every spatial processor maps cleanly to a convolution filter. Two other modes in the same program exposed the limits:
- The volume-reducing mode changed gain with input amplitude and took time to recover. That's level-dependent, time-varying behavior. One fixed impulse response can't describe the response at all levels and across its recovery cycle.
- Another spatial mode changed between repeated tests. Its behavior wasn't stable enough in time for a single fixed response to reproduce consistently.
These results are useful because they define the boundary. A fixed convolution filter works when the processor's response is stable, linear, and repeatable. If the behavior depends on recent signal history, amplitude, or some shifting internal state, you either need a model that represents that dynamic behavior - or accept that a static filter will only approximate one operating condition.
I loaded the four responses into Saq, and it produced the same kind of placement I had been trying to recreate by hand. The point wasn't that convolution is magically better than other audio techniques. It was that the measurements showed this particular mode can be represented that way.
If you want to experiment, compare the processed and unprocessed audio at a matched listening level. A louder version can sound "better" even when most of the difference is just gain. Listen with headphones, because cross-channel timing and level differences are central to the effect. The original project also provides a browser-based comparison; whether you can use tab capture depends on browser support, and the source notes a limitation in Firefox related to getDisplayMedia.
Conclusion
The biggest step was changing the question. Instead of trying to guess which psychoacoustic techniques the commercial effect might use, I measured what it actually did. Impulses, sweeps, tones, paired-channel tests, and repeated recordings each answered a different question about linearity, frequency response, timing, and stability.
The result was a four-filter convolution model for the processor's stable surround mode - and solid evidence that its other modes don't fit that same model. That's the real value of measurement: it tells you what you can reproduce, and where a static modeling approach stops making sense.
If you're building something similar, start with a known input, test the assumptions, and validate on audio that wasn't used to derive the model. Then pick the simplest model that explains the behavior you care about. You can try the Linux implementation in Saq and compare it to your own audio. The goal isn't to reconstruct an unknown implementation for its own sake - it's to build something that behaves well enough to use.