u/Amit31456

When a client hands you footage with bad audio, the order you apply fixes matters more than the tools you use. Getting this wrong either costs you time or makes the problem worse.

Here's the hierarchy that's worked for me:

Step 1: Cut obvious problems before processing Remove the worst sections - the chair scrape, the phone notification, the cough. No noise tool handles impulse noise well. Cut first.

Step 2: Deal with the noise floor Broadband noise (room tone, HVAC, street) responds well to modern deep learning suppression. Tools trained on speech priors (DeepFilterNet, RNNoise) handle this better than FFT-based spectral subtraction because they understand what voice is supposed to sound like. Key: don't over-suppress. Set attenuation limits. The metallic artifact is always suppression applied too aggressively.

Step 3: Filler words and silences, if the client wants them This is where most editors give up because it's mechanical and slow. Word-level timestamps from Whisper-based transcription let you review and cut in bulk rather than scrubbing. Still needs a human review pass but dramatically faster than listening through.

Step 4: Loudness normalization last -16 LUFS for online delivery. Do this last, after all cuts, so your loudness measurement reflects the final edit.

What's the messiest client audio rescue you've had to do? And what's your go-to tool for step 2 right now?

I've been talking to a lot of academics about audio recording workflows, lecture capture, qualitative research interviews, oral history projects, online course production. A few things that come up constantly:

The lecture capture problem University AV systems record at acceptable quality in the lecture hall but the audio is often unusable for anything else - room reverb, HVAC noise, distance from the mic. Running noise reduction in post is now fast enough that it's worth doing before you upload to your LMS.

Research interview audio Field interviews, Zoom recordings, recordings in participants' homes - the audio quality is wildly inconsistent. Transcription accuracy (whether you're using Whisper, Otter, or anything else) drops significantly on noisy recordings. Cleaning the audio before transcription meaningfully improves transcript quality, something most research workflows skip.

The cloud tool problem for IRB-covered research If your research is IRB-approved with a data management plan, uploading participant audio to a commercial cloud service for processing may violate your DMP. Worth checking before you run it through an online tool.

Filler words in qualitative data If you're working with interview transcripts for discourse analysis or thematic coding, the um/uh count actually matters. Automated filler detection gives you a consistent, reproducible count rather than manual annotation, useful if you're reporting on speaking patterns.

What does your current audio-to-transcript workflow look like? Particularly curious if anyone has found a clean solution for noisy field recordings.

Something worth thinking about if you're using AI tools to clean up interview audio:

Almost every browser-based audio enhancement tool - Adobe Podcast Enhance, Cleanvoice, Auphonic, and others - processes your file on their servers. You upload the recording, they run inference on their infrastructure, you download the result.

For most content that's fine. But consider what that means for:

• Confidential source interviews
• Off-the-record conversations you recorded with consent
• Whistleblower audio
• Anything under legal hold or in active litigation
• Interviews in jurisdictions with strict data residency laws (GDPR)

Your source consented to you recording them. They did not consent to that recording being uploaded to a US-based cloud provider's server for processing. In most jurisdictions that's a grey area at best.

The alternative - running audio cleanup entirely on your local machine with no network connection, exists and has gotten genuinely good in the last 18 months. Apple Silicon makes on-device ML fast enough that you're not waiting hours for a cleaned file.

I'm not saying cloud tools are wrong to use. I'm saying most journalists I've talked to hadn't considered the data chain when they chose their audio workflow.

Has source audio privacy come up in your newsroom's tool policy? Curious whether this is on anyone's radar or if it's still mostly an afterthought.

I've spent the last year deep in audio processing and talked to a lot of podcasters

about what slows their editing down. Here's what comes up over and over - and the

actual technical reason behind each one, not just "get a better mic."

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1. "My recordings sound fine to me but listeners complain about background noise"

Room tone, AC hum, and street sound sit in a frequency range your brain

tunes out after a few seconds. Listeners on earbuds haven't adapted to your

room, they hear it fresh every time. It's not your ears that are wrong,

it's that familiarity masks it for you specifically.

2. "I say 'um' constantly and editing them out takes forever"

Manually cutting fillers in a DAW on a 60-minute interview can take 2-3 hours.

The reason it's so tedious is that fillers aren't evenly distributed, they

cluster around topic transitions and moments of hesitation, so you can't

just batch-find them. You have to listen for context every single time.

3. "I removed noise but now my voice sounds metallic / robotic"

Over-suppression. Most noise tools apply a fixed threshold - anything below

X gets cut. The problem is that quiet consonants (s, f, th) often fall below

that threshold, so they get treated as noise. Adaptive attenuation — reducing

only as much as the signal actually needs, avoids this but is much harder to tune.

4. "My guest audio is always way louder or quieter than mine"

Different mics, rooms, and recording chains produce wildly different levels.

Loudness normalization targeting -16 LUFS (Apple Podcasts / Spotify standard)

fixes this in post, but if you're doing it with a simple peak normalize you're

doing it wrong, peak and loudness are not the same thing and one will mess

up your dynamics.

5. "I record sensitive interviews and I'm paranoid about uploading to cloud tools"

More common than people admit - journalists, therapists, HR podcasters,

legal content. Most AI audio tools send your file to a server. If your

guests haven't consented to that, you're in murky territory.

Worth thinking about before you hit "upload."

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What's the biggest audio issue that's still slowing down your editing?

I'm curious what I'm not seeing on this list.

I've been obsessing over AVD on my Shorts and kept hitting a wall. Thumbnails, hooks, pacing: all dialed in. But retention still dropped hard in the first 3 seconds on some videos.

Turned out the culprit was background noise. Subtle hiss and room echo that I barely noticed while editing, but viewers' ears caught immediately and bounced.

YouTube's algorithm doesn't care why people leave it just sees the drop and stops pushing the video.

So I built AudioClean Pro, a Mac app that cleans audio locally using AI (no cloud, no subscription). You drag in your audio file, it removes background noise, hiss, and mouth clicks, and spits out a clean version in seconds.

Happy to answer questions about the audio-retention connection or the app itself. Has anyone else tracked AVD improvements after cleaning up their audio?

You can have a perfect niche, great editing, and solid thumbnails, and still bleed viewers in the first 10 seconds because your audio sounds like it was recorded in a bathroom.

Faceless content lives or dies by voice quality. There's no face on screen to hold attention. The voice *is* the content.

Common issues I see:
- Background hum/hiss from cheap mics or room noise
- Inconsistent volume levels between clips
- Mouth clicks and breath sounds that distract listeners
- Echo from untreated rooms

YouTube tracks retention closely, and poor audio causes early drop-off, which tanks your distribution before your content even gets a chance.
What’s been your biggest audio challenge when recording voiceovers? And what have you tried to fix

https://preview.redd.it/jg4ywkohmuxg1.jpg?width=1440&format=pjpg&auto=webp&s=f5d160133425fe90d533f0d68128409f5789e333

Hey everyone.

I wanted a fast way to clean up podcast and video audio without paying a monthly subscription or waiting for massive files to upload to a server. So I built AudioClean Pro.

It uses local AI (optimized for Apple Silicon) to strip out background noise and room echo instantly. Everything processes 100% on your device, so it's completely private.

I also just added a new feature where it transcribes your audio locally, highlights filler words (like "um" and "uh") in red, and lets you click which ones you want to auto-cut before exporting.

It's a native Mac app and a one-time purchase (no subscriptions). I'd love to hear your feedback if you edit audio on your Mac!

https://www.audiocleanpro.com

I wasted 3 hours cleaning one podcast episode in Audacity before I realized there had to be a better way

Here's what I learned after testing every major option on Mac:

Audacity - free but brutal. To remove background noise you need to: record silence, get a noise profile, apply reduction, manually hunt filler words one by one. For a 1-hour episode that's 2-3 hours of work. Great if you love audio engineering, painful if you just want clean audio.

Adobe Audition - powerful but $22.99/month and still mostly manual. Also no filler word removal at all.

AI tools - process in minutes, not hours. The tradeoff is usually cost or privacy (most upload your audio to a server).

The biggest thing I didn't realize starting out: the tool you pick changes how long editing takes WAY more than your mic does. A $50 mic with good cleanup sounds better than a $300 mic with bad audio.

I wrote up a full breakdown with a comparison table if anyone wants the details. pm me.

What are you all using for audio cleanup? Curious if anyone has found a workflow that's fast without being expensive.