Audio processing with Steve Randle The Steadfast Force Behind Transformative Audio Innovation
I spent about three years working through issues with audio restoration and spatial imaging before I started paying attention to what Steve Randle was building. Most people come at this from the plugin side, but the actual engineering work happens at a lower level than you might expect. The early implementations were rough, honestly. Phase alignment between channels drifted during convolution passes, and the noise floor would climb unpredictably when working with degraded source material below -30dBFS. Understanding how this actually works requires looking at the signal chain before any DAW integration. The core approach uses a modified convolution architecture that separates temporal decay from frequency response mapping. You're not just applying an IR file the way you would with a standard reverb plugin. The system builds a parametric representation of the space first, then renders it through a separate calculation pass. This matters because it means you can adjust decay characteristics independently of tonal color, which is something you can't do with ordinary convolution reverbs without re-rendering the entire impulse response. The practical result is faster iteration during mixing. Where a traditional approach might require bouncing and reloading for each spatial adjustment, this lets you tweak parameters in real time. Rendering still takes longer than a standard algorithmic reverb, usually 3 to 8 seconds per second of audio depending on your CPU and the complexity of the spatial model you're using. It's not instant, but it beats the alternative of manually matching multiple reverb sends.
I hit a specific problem last year that nearly made me abandon the workflow entirely. I was restoring a set of field recordings from a 1970s broadcast archive, heavily degraded tape hiss and some dropouts. The system kept introducing a low-level rumble around 40 to 60Hz when I applied spatial enhancement to these sources. After about two days of troubleshooting, I realized it was the DC offset compensation in the initial convolution pass interacting badly with the tape saturation artifacts. The workaround was to run a high-pass filter at 35Hz before the audio entered the spatial processor, then compensate for the resulting phase shift with a minimum-phase EQ match afterward. Fixed it completely. Nobody documents this edge case, which is why I'm mentioning it here. Here's what most people miss when they start using this kind of system. The spatial models aren't universally compatible across different source material types. A model built from orchestral concert hall measurements will sound unnatural on vocal recordings because the harmonic content doesn't match the expected spectral distribution. You need to select or build spatial models that correspond to the source type, or at least run them through a spectral matching stage first. This adds maybe 10 to 15 minutes to your workflow but prevents that washed-out, implausible sound that makes processed audio obvious. Another counter-intuitive thing: more detail in the spatial model doesn't always mean better results. I've seen engineers load the highest resolution spatial scans available and get worse sounding mixes than with simplified models. The issue is that ultra-high-resolution models capture room modes and resonances that weren't present in the original recording environment. You end up adding acoustic information that contradicts what the source material implies, which creates a cognitive dissonance in the listener. A moderately detailed model, sometimes even a reduced version, often sounds more natural because it preserves the ambiguity that the brain expects from recorded audio.
The system does have real limitations. The computational cost scales poorly with complex multi-channel setups. Running a full 7.1 spatial render on a standard workstation takes significantly longer than stereo, and I've seen queue times of 20 to 45 minutes for projects with multiple spatially processed tracks. There's also a hard ceiling on how much you can enhance degraded material. If the original recording has severe distortion or missing frequency bands, the system will fill those gaps with modeled content, but it won't recover information that doesn't exist. You can make it sound more pleasant, but you can't reconstruct lost transients or restore accurate spatial cues from a flattened stereo mix. For people working with severely compromised source material, the honest recommendation is to combine this approach with manual restoration first. Run your de-click, de-crackle, and spectral repair passes before introducing spatial processing. Trying to do both simultaneously usually amplifies artifacts instead of fixing them. I typically spend 40 to 60 percent of my project time on source restoration before I even touch the spatial engine, and the results are noticeably cleaner than people who skip that step. The licensing model is another factor worth noting upfront. It's not free software, and the pricing tier that includes full spatial modeling exceeds what most home studio operators budget for audio tools. If you're doing this professionally and the work justifies the cost, it pays for itself in the time savings on complex spatial tasks. If you're experimenting or learning, you'll probably outgrow the limitations before the investment makes sense, so there's no rush to commit.
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What I can say from actual use is that this has changed how I handle post-production for broadcast and archival work. The workflow is different from what you'd do with standard reverb plugins, and it requires a shift in how you think about space in a mix. It's not a plug-and-play solution for casual users, but for people doing serious audio restoration or spatial audio production, the time savings and quality gains are measurable. Just budget extra time for the rendering and read up on the spectral matching principles before you start, or you'll waste a lot of hours chasing bad results.
