Fullband Blog
The technology behind audio denoising: how noise suppression models work, what separates them in listening tests, and where to start when you want to remove background noise from your recordings.
Old tools followed fixed rules. A model learns what speech looks like and decides what to keep. This article walks through what happens between upload and download, then shows a before and after with spectrograms on a recording where a train rumble sits under the voice.
Read the full articleHiss is broadband, hum is tonal. This guide separates the two, lists the hardware fixes that come first, then tests two free denoisers on a 28 second recording with a strong machine-like hum: Fullband and RNNoise, published as audio so you can judge for yourself.
Read the full articleScores do not tell you what a denoiser sounds like. We ran ten open speech enhancement models, from tiny real-time networks to heavy offline ones, on the same 28 second recording of speech against loud street traffic, and published every result as audio. No trimming, no loudness changes, no ranking table.
Read the full articleFan hum, room tone, the rumble of a train: steady background noise is the easiest kind to remove, and you do not need a desktop app to do it. We cleaned the same 30 second metro recording in Fullband and Audacity, put the results side by side, and measured what happened to the voice.
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