Background noise identifier. Upload audio and AI tells you what ambient sounds are in your recording.
Select the AI model for audio analysis. Different models may have different capabilities.
Record audio directly from your microphone
To identify the background noise in a recording, upload the audio and the AI names each distinct background sound and its likely source: HVAC hum, traffic, refrigerator, fluorescent buzz, fans, sirens, wind, rain, pets, or the neighbors. For every noise it estimates the approximate frequency range it lives in, rates its severity in the mix (barely audible, noticeable, or distracting), and states whether it can be removed with a noise gate, an EQ cut, or a noise reduction plugin, including roughly what frequency to target. It finishes with recording fixes that would prevent each noise next time, which is the cheaper solution than cleanup. The output is deliberately practical and producer-friendly: not just there is a hum, but a 60Hz electrical hum with harmonics, noticeable, cut with a notch filter at 60 and 120Hz, and unplug the fridge or move the mic next session.
Upload your recording and the AI separates the foreground from everything contaminating it. Source identification names each background sound specifically, distinguishing an HVAC rumble from traffic from a refrigerator cycle, since they need different fixes. Frequency mapping estimates where each noise lives in the spectrum: low-frequency rumble around 40 to 120Hz, broadband hiss up high, midrange chatter in the speech band. Severity rating tells you whether each sound is barely audible, noticeable, or actively distracting, so you know what is worth fixing. Removability assessment matches each noise to the right tool (a gate for noise between phrases, an EQ notch for steady-pitch hums, spectral noise reduction for broadband sources) with the approximate frequency to target. Prevention notes explain the recording-stage fix for each: mic placement, room choice, turning the offending appliance off.
Upload the audio and the identifier names each background sound with a likely source: HVAC hum, refrigerator cycle, traffic, fan, fluorescent buzz, rain, pets, neighbors. Each one gets an approximate frequency range, a severity rating in the mix, and the right removal tool, so you know what it is and what to do about it.
Depends on the noise, which is exactly what the assessment answers. Steady-pitch hums respond to EQ notches, noise between phrases to a gate, broadband hiss to spectral reduction, but noise sharing the speech band with your voice often cannot be cleanly separated. The per-sound verdict tells you when re-recording is honestly cheaper.
Each noise type lives in a characteristic range: electrical hum sits at 50 or 60Hz plus harmonics, HVAC rumble in the low end, hiss up high. The analysis estimates where each identified sound sits in your specific recording and suggests roughly where to aim a notch or cut, which beats sweeping an EQ blind.
Source identification is an inference from acoustic character, so a refrigerator and a distant HVAC unit can genuinely be confused, and frequency estimates are approximate starting points rather than measurements. Where it earns its keep is triage: naming the probable culprit, rating what is actually distracting, and matching each problem to the right fix.
Yes, the prevention notes cover the recording-stage fix for each sound: unplug or pause the appliance, move the mic off-axis from the window, close the HVAC vent, record at a quieter hour. Prevention gets emphasis because a clean take always beats cleanup; noise reduction carries a quality cost.
That is the core use case. Podcasters get producer-style notes (what each noise is, how bad it is, the plugin move that fixes it), and anyone recording voiceover, lectures, or interview audio can run a ten-second room tone clip before a session to catch problems while they are still preventable.
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