I often have voice recordings with a lot of background noise (e.g. a public lecture in a room with poor acoustics, recorded from a phone in the audience — there's usually sounds of paper rustling, noises from the street, etc). Is this "source-separation" the sort of thing that could help, or does anyone have other tips? The best thing I have so far is based on this https://wiki.audacityteam.org/wiki/Sanitizing_speech_recordi... —

(1) Open the file in Audacity and switch to Spectrogram view, (2) set a high-pass filter with ~150 Hz, i.e. filter out frequencies lower than that (which tend to be loud anyway), (3) don’t remove the higher frequencies (which aren’t loud), because they are what make the consonants understandable (apparently), (4) look for specific noises, select the rectangle, and use “Spectral Edit Multi Tool”.

But if machine learning can help that would be really interesting! This Spleeter page does mention “active listening, educational purposes, […] transcription” so I'm excited.