Problem-specific modules
Noise, hum, echo, sibilance and breaths are different problems. Keep each control separate and expose only verified processors.
Clean Up a Voice Recording
Load a spoken recording, identify the main problem and apply only the verified cleanup modules it needs. Compare several passages before exporting a separate processed copy.
The term AI, model names, measurable quality scores, detected noise labels and processing limits must be proven by implementation. Remove decorative before/after scores that are not computed from the file.
Read the full guide1Add audioor click to choose a file
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2Cleanup controlsClean and enhance voice
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3Output settingsDownload cleaned voice
Add audio
Supported formats: MP3, WAV, M4A, AAC, OGG, Opus, FLAC
Drop an audio file here
Supported formats: MP3, WAV, M4A, AAC, OGG, FLAC
Cleanup controls
Settings explained
Noise reductionTargets a learned or estimated noise profile. Strong settings can create musical-noise, watery or gated speech.80%
Voice enhancementEnhancement should improve intelligibility without claiming to reconstruct missing detail or identify every speaker.75%
Hum removalUses notches or a dedicated filter around mains-related frequencies and harmonics. The frequency must match the implementation and region/use case.70%
De-esserReduces strong sibilant frequency energy when detected. Too much processing can make consonants dull or lispy.65%
Original
Waiting
Detected issuesSpeech level
0:00PreviewAudio
0:00 / 0:00
Cleaned
Before and after
Play cleaned result
0:00PreviewAudio
0:00 / 0:00
Before and after
- Original size
- -
- Estimated size
- -
- Noise reduction
- -
- Duration
- 0:00
- Format
- MP3
Practical tips
- Fix microphone position, room noise and gain at recording time whenever possible.
- Use the lowest processing amount that solves the dominant problem.
- Compare at matched loudness; a brighter or louder result is not necessarily cleaner.
Download cleaned voice
Output settings
When confirmed by the implementation, files selected from your device are processed in this browser and are not uploaded to AppKiro.
What can a voice cleaner realistically improve?
A voice cleaner can reduce certain steady noises and speech artefacts when the required processor is implemented. It cannot recreate words hidden by loud competing sound, repair severe clipping or turn a distant reverberant recording into a studio microphone recording.
What this tool lets you do
Conservative speech enhancement
Enhancement should improve intelligibility without claiming to reconstruct missing detail or identify every speaker.
Before-and-after review
Compare multiple quiet and loud passages; a single flattering sample can hide pumping or removed consonants.
When to use it
- Improve a voice memo recorded near a fan or air conditioner.
- Reduce mains hum or low-frequency electrical noise when the dedicated filter supports it.
- Tame harsh sibilance in narration with a controllable de-esser.
- Reduce mild room echo or breaths before transcription and publishing.
- Prepare interviews, lessons, podcasts or voice-over drafts for further editing.
How to use it
Identify the main fault
Listen with headphones and decide whether the dominant problem is steady noise, hum, echo, sibilance, breaths or level.
Analyze the recording
Let the implemented detector inspect enough speech and noise; avoid relying on a few milliseconds of audio.
Enable one module at a time
Start at low strength, compare the original and cleaned signal, then add another processor only if needed.
Export and re-check
Save a new file, then confirm intelligibility, natural tone, noise movement and playback compatibility.
Settings explained
Noise reduction
Targets a learned or estimated noise profile. Strong settings can create musical-noise, watery or gated speech.
Hum removal
Uses notches or a dedicated filter around mains-related frequencies and harmonics. The frequency must match the implementation and region/use case.
De-esser
Reduces strong sibilant frequency energy when detected. Too much processing can make consonants dull or lispy.
File handling and privacy
Audit local and remote processing separately. If a speech model, denoising API, model download, telemetry or crash report contains media or derived features, disclose it. A downloaded model can still run locally; URL mode still contacts its source.
Limits to understand
- Speech-like background sounds are difficult to separate from the wanted voice.
- Severe clipping, dropouts and words masked by louder audio cannot be reliably reconstructed.
- Aggressive cleanup can remove consonants, room character and natural breaths.
- Model output and speed vary with browser, device, recording quality and implemented fallback path.
Practical tips
- Fix microphone position, room noise and gain at recording time whenever possible.
- Use the lowest processing amount that solves the dominant problem.
- Compare at matched loudness; a brighter or louder result is not necessarily cleaner.
- Keep an untouched source and use a lossless intermediate when additional editing follows.
Troubleshooting
The voice sounds robotic
Reduce noise reduction or enhancement and check whether an unsupported fallback is being used.
Sibilance is now dull
Lower the de-esser amount or narrow its verified target range.
Echo remains
Strong late reverberation cannot always be removed. Use a closer microphone or a better source when available.
Noise pumps between words
Use less reduction, improve the noise estimate or retain more natural room tone.
Frequently asked questions
Is this the same as removing all background sound?
No. Cleanup is most reliable for certain steady or well-characterized noise; changing voices, music and traffic can overlap the wanted speech.
Does “AI model” mean the tool understands my words?
Not necessarily. A denoising model may estimate speech-like and noise-like components without transcription or semantic understanding.
Can it fix a clipped recording?
No. It may reduce other artefacts, but flattened clipped samples cannot be reconstructed reliably.
What is the difference between echo reduction and noise reduction?
Noise reduction targets unwanted background energy; echo reduction targets delayed room reflections. They require different processing.
Should I enable every control?
No. Enable only modules that address an audible problem and compare after each change.
Why is there a metallic sound after cleaning?
The processing is too strong, the noise changes over time or the model is mismatched. Reduce strength and review several passages.
Does the original file change?
No. The tool should render a separate cleaned copy.
Are recordings uploaded?
Only state local processing after checking models, downloads, inference, preview, encoding, telemetry and storage. URL mode contacts the source host.