Remove background noise from audio with AI

Drop in any recording, hear the before & after in seconds. Free, no signup.

🎧

Drop an audio or video file here

or

MP3, WAV, M4A, FLAC, OGG, AAC, MP4, MOV

Gentle Aggressive

Lower it for a light touch when the audio is already fairly clean.

Cleaning your audio…

Before

Tip: press the space bar to toggle Before / After.

Download cleaned audio Sign up for full-length files

Free demo cleans short clips. Sign in for full-length files, all formats and every tool.

✓ No install, works in your browser ✓ Keeps your voice natural ✓ Files deleted automatically
29
dedicated tools
< 60s
typical clean time
100+
languages supported
8+
audio & video formats

How it works

1

Upload your audio

Drop in an MP3, WAV, M4A or even a video file — we extract the audio for you.

2

AI cleans it

Our models remove the noise while keeping your voice natural — no settings to fiddle with.

3

Hear the before & after

A/B the original against the cleaned track, then download in your chosen format.

Frequently asked questions

Yes — you can clean short clips for free with no signup. Sign in for full-length files, every tool and all output formats.

Yes. Our denoisers apply a soft per-frequency gain rather than gating whole sections, so the noise floor drops while your voice keeps its natural tone.

MP3, WAV, M4A, FLAC, OGG and AAC audio, plus MP4 and MOV video — we extract and clean the audio track.

Your upload is used only to produce the cleaned result and is deleted automatically afterward. We never use it to train our models.

Each tool runs a dedicated pipeline on our own GPUs: your file is decoded, the audio passes through a model tuned for that one problem, and the cleaned track is returned in seconds. The AI engines under the hood are best-in-class open-source models — DeepFilterNet for denoising, Demucs for vocal and stem separation, and VoiceFixer for restoration — plus our own signal-processing filters for hum, clicks and loudness. The service itself isn't open source, but we build on and credit the open models that make it possible.