Rooms and reverb — taking the echo away

Record someone in a tiled bathroom and they sound like they’re in a tiled bathroom. Record them in a small carpeted booth and they sound “close” and “dry.” The difference is reverb — the thousands of tiny echoes a room adds as sound bounces off the walls, floor, and ceiling before it reaches the mic.

What reverb really is

When you speak, the mic hears two things. First the direct sound — your voice travelling straight to it. Then, a few thousandths of a second later, a flood of reflections — the same sound arriving again and again, having bounced around the room, each copy a little quieter and a little later than the last. That trail of fading echoes is reverb. A big stone hall has a long, obvious trail; a small treated studio has almost none.

Reverb is the trickiest “reducer” in this book, because the echoes are made of the exact same sound as the voice — they’re just delayed, quieter copies. You can’t separate them by pitch the way you separate hiss, because they share the voice’s pitches entirely.

The trick: watch how each pitch fades

So de-reverb uses timing instead of pitch. Here’s the insight. When you start a new word, the direct sound arrives as a sharp onset — a quick rise in energy. Then you stop, but the room keeps ringing: the energy at each pitch decays away in a smooth, tell-tale tail. That decaying tail is the reverb.

A de-reverb tool watches each pitch over time and learns the difference between the punchy onsets (keep these — they’re the real voice) and the lingering decay tails (turn these down — they’re the room). In effect it follows the energy at every pitch and, whenever the energy is just coasting downward toward the room’s background level, it gates it back. The direct, intentional sound survives; the ringing afterglow is suppressed.

Cathar does exactly this with a two-pass scan: first it measures how low each pitch typically sinks (the “reverb floor”), then it gently gates anything sitting near that floor. The --strength knob controls how aggressively it chases the tails.

Why it’s never perfect

Two honest limitations:

• Onsets and tails overlap. Fast speech starts a new word before the previous one’s tail has died, so the tool is always making a judgement call, and pushed hard it can make a voice sound a bit hollow, gated, or “phasey.”
• You can dry a room but not delete it. De-reverb shortens and softens the trail; it can’t put you in a different room. Targeting a modest improvement — “less boomy,” “a bit closer” — gives far nicer results than chasing total removal.

How the big tools do it

• iZotope RX’s “De-reverb” is the leader, and the gap here is large. It uses a learned model of the reverb tail and, in recent versions, machine learning to separate dry voice from room — it can take a startling amount of reverb off a voice while keeping it natural. There’s a separate “Dialogue De-reverb” tuned for speech.
• Acon Digital and Accentize make well-regarded dedicated de-reverb plug-ins used in film post-production, several now ML-based.
• Audition has a “DeReverb” effect; Audacity has no real built-in de-reverb, which tells you how much harder this problem is than hum or hiss.

This is the area where classical, no-model tools like cathar are most outclassed by modern ML, because separating a sound from delayed copies of itself is exactly the kind of “needs a trained ear” task that a learned model does best. Cathar’s gate-the-tails approach gives a real, useful reduction on moderate reverb; for heavy, film-grade de-reverberation, RX is in a different league.