AI audio enhancement on podcast recordings: a case study

General AI Discussions
1

I’ve been running a semi-regular podcast for about a year as a side project alongside client work. Recording conditions are inconsistent because I record from wherever I happen to be, which could be a hotel room in Lisbon or an Airbnb in Chiang Mai. Not ideal acoustics.

I spent the last month testing AI audio enhancement tools on a batch of old recordings to see whether I could retroactively improve quality. Here’s what I found.

Background noise removal: genuinely excellent. The kind of ambient room tone that used to require either a treated room or significant post-production effort is handled automatically and cleanly. This alone would have saved me hours of work across the year.

Voice clarity improvement: good for consistent issues, worse for variable ones. If the recording has a consistent quality problem, low gain, slight distortion, the tools correct well. If the quality varies within a recording, which happens a lot when I’m moving or the environment changes mid-session, the tool sometimes overcorrects in ways that create audible artifacts.

The overcorrection problem: this is the main thing. When the tool is working hard on a particularly difficult section, it occasionally produces audio that sounds slightly processed in a way that most listeners would notice if they were paying attention. It’s not bad, but it’s not natural either. Paradoxically, the worst source material sometimes produces the most ‘enhanced’ sounding output rather than the cleanest.

My current use: background noise and basic clarity as a standard step. I skip the aggressive enhancement on anything that sounds close to acceptable already. Net result is better-sounding output for about 40% of my episodes with no meaningful time investment.

2

The artifact introduction on difficult source material is a known issue with current audio enhancement models. They’re trained on what ‘good audio’ sounds like and they push toward that target regardless of whether the transformation is achievable without introducing artifacts. It’s a quality ceiling problem, not a failure of the base approach.

3

The 40% improvement figure is a useful benchmark. I’ve been evaluating whether something similar would be worth adding to our audiobook production workflow and the variable-environment caveat is exactly the condition I needed to understand before making that call.

4

The variable environment problem is the main reason I haven’t committed to these tools for anything client-facing. When recording conditions are controlled, enhancement tools are great. When they’re variable, you introduce unpredictability at exactly the point where you need consistency.

5

The overcorrection artifact problem is one I’ve heard from other audio-heavy content creators. The tools seem to have a target output profile they’re working toward and when the source material is too far from that profile, the correction overshoot becomes audible. Useful to know the threshold exists before committing to the workflow.

6

From a non-audio production standpoint, the ‘skip aggressive enhancement on acceptable source material’ rule seems like good general guidance for AI enhancement tools in any medium. The instinct to improve everything often produces worse results than leaving good-enough things alone.