Volume Booster & Normalizer
Boost with a manual dB gain, normalize to an exact peak, or match average loudness across recordings — with a soft limiter that tames peaks instead of clipping harshly, live before/after readouts, and instant A/B comparison before you export.
Quick Tips for a Clean Boost
Features
Three Different Ideas of "Louder"
"Make this louder" turns out to mean genuinely different things depending on what's actually wrong with a recording, which is why this tool offers three separate modes instead of one slider. Manual Gain is the most direct — you decide the exact decibel amount and it applies uniformly, which is right when you already know precisely how much boost a file needs, often from experience with similar recordings.
Normalize Peak and Normalize Loudness both work automatically from a measurement rather than a number you choose, but they measure different things. Peak normalization cares about the single loudest instant in the file and scales to put that instant at an exact target — mathematically guaranteed never to clip, since nothing in the file exceeds that one controlling peak. Loudness normalization instead measures the average energy across the entire file and matches that average to a target, which is a much closer approximation of how loud a recording actually sounds to a human ear over its full length, not just at its single loudest moment.
Why a Boost Can Sound Worse Than the Original
Digital audio has a hard ceiling — a sample can't represent a value louder than full scale, and pushing past it without any protection doesn't produce a slightly-too-loud sound, it produces a harshly clipped, crackling one, because the waveform's peaks get chopped flat instead of continuing their natural curve. This is the single most common way a well-intentioned volume boost backfires: a gain that sounded reasonable on the loudest section of a file turns out to clip badly the moment a louder passage arrives later on.
The soft limiter exists specifically to prevent that outcome without requiring you to guess a perfectly safe gain value in advance. Rather than a hard ceiling that slices off anything too loud, it compresses smoothly as a signal approaches full scale — audio comfortably below the threshold passes through completely untouched, while anything pushing close to or past the ceiling gets gently reined in instead of clipped outright. The practical result is a boost that can be pushed harder before anything sounds broken.
Peak vs. Perceived Loudness
A recording with one loud handclap and otherwise quiet, conversational speech has a peak that's misleadingly high relative to how loud the recording actually feels to listen to — normalizing to that peak would barely change anything, since the file already "uses" its available headroom at that one instant. Loudness normalization solves exactly this mismatch by looking at the average level across the whole recording instead, which is why it's the better choice for voice content specifically: podcasts, voiceovers and lecture recordings tend to have far more consistent dynamics than music, so their average level is a genuinely representative measure of how loud they'll feel throughout.
It's worth being precise about what this measures, too — it's RMS-based, a straightforward average of signal energy, not the frequency-weighted, gated LUFS measurement broadcasters and streaming platforms use for certified loudness compliance. RMS normalization gets everyday files sounding consistently loud against each other very effectively; it just isn't the exact standard a platform's loudness target may specifically require.
Listening Before You Commit
Every adjustment here is designed to be checked, not just trusted — Play Current and Play Original sit next to each other specifically so switching between the source file and the boosted result takes one click rather than requiring you to remember what the original sounded like a few minutes ago. The peak and loudness numbers update alongside whatever you hear, so a boost that sounds right can also be confirmed numerically before committing to an export.
Clicking Apply doesn't lock anything in permanently, either — it commits the current adjustment as the new working version and lets you keep going, whether that means a small manual correction after normalizing, or starting over completely with Reset to Original if a setting didn't turn out the way you expected.
Who Actually Needs to Boost Audio Volume
Podcasters normalize every guest's segment to the same loudness so listeners aren't constantly reaching for the volume knob between speakers. Musicians boost a quiet phone-recorded demo enough to actually hear the details clearly on playback. Video creators fix a voiceover track recorded too quietly against louder background elements. And plenty of people just have one old voice memo, recorded from across a room, that's nearly inaudible at normal volume.
A podcaster with three separately-recorded guest tracks runs each one through Normalize Loudness at the same target level before combining them, so no single voice comes through noticeably louder or quieter than the others — then brings the matched tracks into the Audio Merger to join them into one episode. A musician instead boosts a quiet rehearsal recording with Manual Gain, checking the headroom number first to land right at the edge of clipping without going over, then trims the unusable warm-up seconds off the front with the Audio Trimmer & Cutter before sharing the result.
What Volume Boosting Can't Fix
Turning something up amplifies everything in the recording equally — the parts you want louder and any background hiss, hum or room noise right along with them. A boosted recording of a quiet, noisy source ends up as a loud, noisy one, not a clean one; genuine noise reduction is a fundamentally different kind of audio processing that this tool isn't attempting to do.
It also can't repair distortion that already happened before the file reached it — a recording that clipped at the microphone or the recording device's own input stage carries that damage permanently in the file itself, and no amount of careful gain staging afterward can undo audio information that was never captured correctly in the first place. For everything else — a recording that's simply too quiet, or several files that need to sit at a consistent, comfortable volume next to each other — it handles the job cleanly and holds up well against paid desktop normalization tools doing the same basic work.