How to Clean Up Audio on iPhone: Vocals, Noise and Podcasts

Three jobs, one idea

People usually arrive here with one of three jobs: take the vocals out of a song, pull out one instrument, or clean the noise off a voice recording or podcast. They look different, but the idea underneath is the same. A model estimates which parts of the sound belong to what you want and which don’t, then separates them. That estimate is good when the recording gives it something to work with, and shaky when it doesn’t. Knowing that makes it much easier to pick the right tool and judge the result.

Where the processing runs matters

Most audio tools fall into two camps. Cloud tools upload your file to a server, process it there and send the result back. On-device tools do the work on your phone.

• Cloud strengths. Larger models can be more capable, and your phone does less work.
• Cloud costs. Upload time, a queue, an account, often a subscription, and your audio sits on someone else’s server. That matters for unreleased tracks, client work and interviews under embargo.
• On-device strengths. Processing starts straight away, works offline and in Airplane Mode, and nothing is uploaded.
• On-device limits. A phone has finite power and memory, so results can differ from the biggest cloud models. Test on your own material before trusting it for a deadline.

There isn’t a universal winner. Choose by how sensitive the audio is and how good the result needs to be.

Remove vocals or extract one instrument

This is called stem separation: splitting a mix into parts such as vocals, drums, bass, piano, guitar and everything else. The steps are similar in any app:

1. Import the track from Files or your Photos library.
2. Choose which stems you want, one or several.
3. Let it process, then preview each stem on its own.
4. Export the stems separately, or everything except the part you removed.

What to expect is the useful part. Separation is never perfect. You will often hear a little bleed of the removed part, a watery or phasey sound on the vocal, or reverb tails that refuse to separate. Start from the best file you have: a lossless or high-bitrate copy gives the model more to work with than a heavily compressed one. Dense, busy mixes are harder than sparse ones, and old mono recordings are the hardest.

One legal note, and we are not lawyers: making a backing track to practise with is a different thing from publishing a remix of a song you don’t own the rights to. If you plan to release something, check the rights first.

Clean background noise from a voice recording

Not all noise is equal, and that decides how well cleaning works.

• Steady noise is easiest. Air conditioning, fans, hiss and the hum of a room are consistent, so a model can learn them and subtract them.
• Speech-like and sudden noise is hardest. Chatter in a café, a passing train or a door slam looks a lot like what you want to keep, so it is much harder to remove without touching the voice.
• Too much cleaning sounds worse. Push it too far and the voice turns thin, robotic or underwater. Preview the result and prefer a little noise over damaged speech.

The best fix is still at the source. Get the microphone closer to the speaker, turn off the fan if you can, and record in a room with soft surfaces. Even a ten-second test recording before you start tells you what the room sounds like. No tool can fully rebuild clarity a low-quality mic never captured.

A clean-up order for podcasts

For a podcast, the order of operations makes a bigger difference than the tool.

1. If you recorded a separate track for each speaker, clean each one on its own. Cleaning separate tracks gives better results than processing a pre-mixed file.
2. Preview each cleaned track for over-processing before you export.
3. Bring the cleaned tracks into your editing app to cut, mix, add music and level the speakers.
4. Check loudness at the end against your platform’s current guidance. Many podcasters aim around −16 LUFS, but targets differ by platform and change, so check yours.

A noise cleaner is a step before editing, not a replacement for it. It doesn’t mix tracks, add music or balance volume between speakers.

Common mistakes

• Judging a tool on one lucky clip. Test on your own worst recording.
• Processing a pre-mixed podcast file instead of the separate tracks.
• Cleaning so hard that the voice sounds processed.
• Starting from a heavily compressed copy when a better one exists.
• Uploading unreleased or sensitive audio to a service without checking where it goes.

Tools mentioned

We make three iPhone apps that do these jobs, so treat this as a disclosure rather than a recommendation: OffgridStem separates a track into vocals, drums, bass, piano, guitar and other instruments, OffgridVox reduces background noise in voice recordings, and OffgridScribe transcribes and summarises them. All three process audio on the device, work offline and are one-time purchases rather than subscriptions. They are not the only options, and our own comparison of stem splitters is linked below.

More from Offgrid Studio

OffgridStem vs. Moises, LALAL.AI, StemSplit, and AudioShake: Which Stem Splitter Keeps Your Audio Private?
What Popular Transcription Apps Actually Do With Your Recordings
How to Check If an iPhone App Is Secretly Uploading Your Data (Free, 15 Minutes)