01
Identify what is wrong with the recording
Listen for steady hum, transient bumps, room echo, inconsistent speaker level and clipping. Note whether each issue occurs during recording or can be fixed afterward.
Why this matters: Different tools are designed for different noise classes and processing stages.
02
Improve the source before applying AI
Where possible, reduce microphone distance, eliminate nearby mechanical noise or re-record clipped segments. Preserve the original file.
Why this matters: Some distortions cannot be reliably reconstructed by post-processing.
03
Process a difficult excerpt
Try one representative excerpt, then compare it at equal playback volume with the original. Check sibilants, consonants, room tone and background music.
Why this matters: Overprocessing can introduce metallic artifacts or remove parts of natural speech.
04
Finish and export at a consistent level
Check target loudness, mono/stereo output, the editing workflow and the provider's upload or processing limits. Keep enough headroom and an archived original.
Why this matters: An apparently cleaner sample is not useful if the final file sounds uneven or cannot be exported at the required quality.