Cleaning up audio with AI

Members will forgive a lot in a recording. A plain background, a slightly awkward camera angle, a lesson filmed on a phone: none of that stops them learning. Bad audio is different. A humming fridge, an echoing room or a guest who is twice as loud as the host makes a recording tiring to listen to, and tired members stop listening.

AI audio tools have made cleanup far easier. Problems that once needed a sound engineer can often be improved with one button. But that button can also make voices sound thin, watery or robotic if you push it too far. This guide explains the common problems in plain terms, what AI can and cannot fix, and how to check the result before it reaches your members.

Name the problem before you fix it

Different problems need different fixes, so start by listening on headphones and identifying what you are dealing with:

• Background noise: a steady hum, hiss, fan or air conditioner under the voice. This is usually the easiest to improve.
• Room echo: the hollow sound of a voice bouncing off bare walls. Engineers call it reverb.
• Uneven levels: one speaker much louder than another, or a voice that swings between quiet and loud.
• Clipping: harsh crackling distortion where the recording was too loud for the microphone. This is the hardest to repair, because the sound was never captured properly.
• Distractions: mouth clicks, a dog barking, a door slamming, popping sounds on words that start with p or b.

What AI enhancement tools do

There are two broad kinds of help:

• One-click speech enhancement. Tools such as Adobe Podcast's Enhance Speech and Descript's Studio Sound process a voice recording to reduce noise and echo and make it sound as if it was recorded in a better room. They can work wonders on a rough recording.
• Individual AI-assisted tools inside audio and video editors: noise reduction, echo reduction, automatic leveling and loudness matching. These give you more control over each step.

Results vary with the recording, so run the same two-minute sample through two different tools and compare them. Listen for what the tool removed as well as what it improved. As with all AI tools, the options change, and a quick retest now and then is worthwhile.

A sensible cleanup order

If you are working step by step rather than with one button, this order avoids most problems:

1. Keep the original. Save an untouched copy before you change anything.
2. Trim the start, the end and any long dead sections.
3. Reduce steady noise such as hum and hiss.
4. Reduce echo, gently.
5. Even out levels so every speaker sits at a similar volume.
6. Match loudness. Loudness normalization sets the overall volume to a consistent level, so your lessons do not play noticeably louder or quieter than each other.
7. Export and listen again.

The most common mistake is over-processing. If the enhanced voice sounds swirly, metallic or strangely flat, reduce the strength. Many tools let you blend the processed sound with the original, and a lighter touch usually sounds more natural.

Use an assistant as your audio coach

If you are not sure which settings to use in your editor, a general AI assistant can walk you through it. Describe the recording and the problem clearly:

I recorded [type of content, such as an interview or lesson] in [describe the room: size, hard or soft surfaces, any appliances running] using [microphone or device]. When I listen back, the problems are [describe what you hear: a low hum, echo, one voice much quieter]. I am editing in [name of your editing tool]. Give me step-by-step instructions to improve this, in the right order, with suggested starting settings and what to listen for at each step to know if I have gone too far. Then tell me what I could change next time I record to avoid these problems.

A good answer gives you a short sequence you can follow with the software open, plus two or three changes to your setup. Check the steps against your tool's own help pages if anything does not match what you see.

Fix it at the source next time

AI cleanup is a rescue, not a substitute for a decent recording. Imagine Hearthside Book Circle, which records monthly author interviews over video calls. A few changes make every later cleanup easier: asking authors to wear earbuds with a microphone, choosing a room with curtains, rugs or a full bookshelf, switching off fans and notifications, and recording ten seconds of silence at the start, which some noise tools use as a sample of the background sound.

If you record with other people, a clean recording also produces a better transcript, as covered in transcribing recordings with AI.

Review before members listen

A person should listen to the cleaned audio before it is published, and not only on studio headphones. Check it:

• On headphones, for artifacts, clicks and any warbling where noise was removed.
• On a phone speaker, where most members will hear it, for overall clarity and volume.
• Against the original, to make sure the voices still sound like the people who spoke. Enhancement should never make a guest sound like someone else.
• At the edges of cuts, if you also edited the content, as described in editing video by editing text.

If a recording still sounds poor after gentle cleanup, consider whether it needs re-recording, or whether a transcript and summary would serve members better than the audio itself.

Your first steps

• Pick the recording in your library that members complain about most, or the one you wince at.
• Listen on headphones and name its problems.
• Try one-click enhancement in two tools on the same short sample.
• Choose the more natural result and process the full file with a lighter touch.
• Check it on a phone speaker and against the original.
• Write down three changes to make to your recording setup.

For simple equipment and technique that improve recordings from the start, see recording course videos without a studio.