At points, the second person’s voice all but blended with and disappeared into the background noise. ![]() Naturally the second voice was softer and roomier than the first by virtue of the mic being closer to the first person’s mouth. There was a reasonable amount of background chatter and intermittent disruptions throughout. In one recording I put through Hush, there were two voices – the person on whom the lapel mic was clipped, and another person walking alongside him. I don’t pretend to understand how this works, but it’s evidently quite different to typical noise reduction plug-ins which use level and frequency content differences to isolate a source within a recording. Hush is clearly trained to zero in on human speech. While the app accepts all types of audio formats I got the best results feeding it WAV files. The processing speed isn’t exactly lightning fast on my Intel iMac but the results are worth the wait. It’s optimised for Macs with M1 and M2 chips running macOS 12 or later. Other than that, pick a destination folder and optional prefix and suffix for the output file, select the audio format, then drag ’n’ drop the audio into the window. ![]() A Mix control is the only real parameter impacting the results. So far, Hush has exceeded my expectations. It’s a noise removal Mac app for recorded speech that’s powered by machine learning.Ĭreator Ian Sampson kindly offered AT a trial version which I have been testing in anger with all manner of noise-riddled dialogue recordings. With AI swiftly invading our everyday lives in the form of ChatGPT and AI-generated artwork, it was only a matter of time before AI spawned some genuinely useful audio tools.
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