You’ve recorded an episode. What happens between that raw file and something you’d actually publish? This post walks through the three-step order we recommend, and — just as important — why the order matters.
Step 1: Remove background noise first
Start with the Background Noise Remover. Home recordings almost always carry a constant noise floor: air conditioning, computer fans, room hum. Removing it first matters because every later step — especially loudness normalization — amplifies whatever is already in the file. If you normalize a noisy recording, you normalize the noise too.
We measured this while tuning the tool. On our test file, running the cleanup with loudness normalization switched on inside the same pass cut the signal-to-noise improvement from +11.1 dB to +2.2 dB, and left the residual noise floor at −37.4 dBFS instead of −52.0 dBFS. Normalization raises quiet content, and the quietest content in a raw recording is the noise. That is exactly why it belongs in a separate, later step.
Pick the gentlest aggressiveness level that solves your problem. Higher levels remove more noise but can leave the voice sounding processed. We measured the difference between all four levels — see our post on noise removal levels for the numbers.
Step 2: Normalize loudness
Next, run the cleaned file through the Podcast Audio Studio. It applies two-pass loudness normalization to a target you choose — Podcast (−16 LUFS with a −1.5 dBTP true-peak ceiling), YouTube or Spotify (both −14 LUFS, −1.0 dBTP) — trims silence from the start and end of the file, and adds a fade in and out.
Loudness consistency is the single most audible difference between a produced-sounding episode and a raw one — more than any effect or EQ. If the terms in that paragraph are unfamiliar, we explain LUFS and true peak here.
One honest note on what it does not do: it trims silence at the two ends of the file, not long pauses in the middle. Removing a rambling gap halfway through the episode is editing, not mastering.
Step 3: Cut clips last
Finally, run the finished episode through the Podcast Clip Cutter to generate short clips for social platforms. Doing this last means the clips inherit the cleaned, normalized audio — you never want to promote your show with a clip that still has fan hum in it.
The clip cutter finds high-energy moments automatically. Treat its output as candidates, not final picks: the loudest moment is often a good one, but not always the best one. Listen before you post. More on how energy detection works, and where it falls short.
Why this order and not another?
Noise removal before normalization: normalization raises quiet content, including noise. Clean first, then raise — the measurement above is what that costs when you get it backwards.
Clips last: any change you make to the master after cutting clips would force you to re-cut them.
What this workflow doesn’t do
It doesn’t edit content. Removing filler words, cutting a rambling section, rearranging segments — that’s editorial work these tools don’t attempt. What they do automate is the mechanical layer: noise, loudness, end-trimming, fades, clip extraction. For many episodes, that’s most of the time spent in post.
All three tools are free, need nothing installed, and accept files up to 200 MB and three hours long, with a limit of 10 jobs per day. Your files are deleted from the server automatically: finished output after two hours, the uploaded source after one.