CLEAR

Supertone CLEAR applies neural network-based source separation to the specific problem of isolated voice cleanup, splitting incoming audio into three discrete channels: clean voice, ambience, and reverb tail. This three-channel architecture distinguishes it from broader stem-separation tools, offering dedicated control over vocal clarity without the collateral processing that affects competing instruments or background elements.

The plugin's separation algorithm demonstrates measurable advantages in artifact suppression compared to earlier generation AI tools. Where conventional de-noisers and de-reversers tend to introduce metallic distortion or phase artifacts, CLEAR maintains vocal integrity while achieving significant noise floor reduction and early reflection removal. The implementation supports both online and offline processing workflows, with a low-CPU mode expanding accessibility for sessions with high plugin density.

The control scheme prioritizes workflow efficiency - three knobs govern the dry/wet balance of each separated channel, supported by visual feedback showing the combined output or individual channel isolation. This straightforward topology makes quick A/B comparisons feasible without menu diving.

CLEAR suits podcast production, post-production dialogue cleanup, and vocal recording refinement where source material contains significant room tone or unwanted reflections. It's particularly valuable for salvaging location recordings or sessions tracked in treated but non-ideal spaces. While not a replacement for proper mic technique or acoustic treatment, it meaningfully extends what engineers can recover from compromised source material.