Austin Shijo wanted to recreate a commercial headphone spatial-audio effect on Linux. Trying plausible filters got him close, then stuck. The breakthrough was to stop guessing at the implementation and measure the processor’s observable behavior instead.
His test recording was designed to answer specific questions:
- Is the effect linear? Compare impulses at different amplitudes and check whether combined channel inputs match the sum of their separate responses.
- Is it time-invariant? Repeat identical probes and look for changes in the output.
- Can a fixed filter reproduce it? Extract four impulse responses for the two-input, two-output stereo system, then validate against a noise segment kept aside.
- How much of the response matters? Compare increasingly long filters rather than discarding the reverberation tail by intuition.
The main surround setting passed these checks closely enough that reconstruction error was comparable to the difference between repeated recordings. Other modes did not: one adjusted gain over time, and another changed between repeats. A fixed convolution filter was a useful model, not a universal explanation.
The transferable lesson is about debugging under limited access: design probes that distinguish competing explanations, preserve the relationships you need to measure, and test the reconstruction on data you did not use to derive it. Knowing the original algorithm is optional; knowing where your model stops working is not.