I have a bunch of phone interviews where there is large sound volume difference between two voices in some of the interview audios, because they were recorded only on one side of the phone. May I ask what is the preferred procedure for reprocessing these audios so they are fit for downstream machine learning feature extraction and modelling tasks while the characteristics of the audio will not be sacrificed?
After some reading on audio compression, normalisation, and levelling, e.g. here, I have to say I am still not sure what to do next. I have seen simple minmax scaling being suggested for normalising the waveform amplitude. I have also been suggested to normalise the audio to a target amplitude (e.g.
pydub.effects or ffmpeg
dynaudnorm), which is loudness normalisation if I am not mistaken. Should I use a compressor or automatic gain controllor? Descript has a auto-leveling feature that seems to be useful, though I can't find any technical info on it.
My current plan, which is the simplest one, is to segment the audio streams to speaker turns, then normalise each speaker turn to (-1, 1) by minmax scaling. How does this sound? Btw any suggestions on declipping? Some of the audios have been clipped. Thanks!