How to improve audio quality using noise reduction and audio cleanup tools

How to in Descript

1. Import media and establish audio baseline: Add your audio into Descript, select the track, and play the unedited clip to document the baseline loudness and noise level. Expected outcome: a reference waveform and identified noisy sections for targeted cleanup. Verification: listen to silent segments and confirm a consistent noise floor.
2. Identify noise sections and set reference noise floor: Scan silent or near-silent portions to understand the noise floor and note where background noise is most evident. Expected outcome: a mapped noise profile to inform cleanup levels. Verification: there is a clear baseline reference before applying denoising.
3. Enable Studio Sound for noise reduction and clarity: Turn on Studio Sound to reduce background noise and enhance vocal clarity. Expected outcome: noticeable reduction in background noise and crisper speech. Verification: perform an A/B compare between pre- and post-Studio Sound audio.
4. Tune Studio Sound for natural tone: Adjust Studio Sound intensity to balance denoising with natural voice texture. Expected outcome: reduced artifacts while preserving vocal character. Verification: listen for any robotic sound and adjust accordingly.
5. Apply high-pass filter to remove low-frequency rumble: Apply a gentle high-pass filter (around 80–100 Hz) to remove rumble without affecting voice fundamentals. Expected outcome: cleaner low end with preserved vocal presence. Verification: check spectral balance in bass region and confirm voice remains clear.
6. Apply EQ to reduce hiss and improve intelligibility: Make light adjustments to mid/high frequencies to tame hiss and improve intelligibility (focus on reducing harshness while preserving natural brightness). Expected outcome: clearer consonants and reduced sibilance. Verification: listen to speech plosives and overall clarity.
7. Apply light compression and normalize loudness: Apply mild compression (e.g., 2:1) to stabilize dynamics, then normalize to a consistent target loudness. Expected outcome: even levels across phrases. Verification: measure peak and LUFS consistency across the track.
8. Verify improvements across playback environments: Review the processed audio on headphones, laptop speakers, and mobile devices to ensure clarity and lack of artifacts across platforms. Expected outcome: consistent perceived loudness and intelligibility. Verification: side-by-side listening comparisons across devices.
9. Export cleaned audio and save workflow: Export the cleaned audio in preferred formats (e.g., WAV/MP3) and save the project version for future reuse. Expected outcome: publish-ready audio; original remains available for reference. Verification: file exports complete without errors and metadata is preserved.