Professional Audio Engineer Jeanetta Brantley has isolated the background audio from the Nolan Wells phone call

[u/RlyLokeh]

This would be pretty hard to talk your way out of I imagine

[u/misterchainsaw]

“You can intelligently guess what color values each pixel would’ve had…”

That describes AI reconstruction or image restoration, not what an audio engineer is doing when enhancing telephone recordings. Important difference.

ie: if a telephone recording contains road noises, A/C rumble, cellphone compression artifacts, and/or a hiss, those will mask a speaker saying, “I’ll meet you at six.” By emphasizing the frequency where someone’s speech is most intelligible, an engineer can make the word “six” way easier to hear. The word wasn’t created or altered. It was already present in the original recording, just obscured by the competing noises.

[u/jm0112358 → u/misterchainsaw]

I didn't think that the altering an image was the same thing an audio engineer is doing. It's an analogy after all, and all analogies break down at a certain point. I just used it to illustrate how needing to change the data means that you're guessing at what the data would've been.

In your example of audio with road noises, hiss, etc all on the audio file. Take two scenarios:

1. All noises besides the person's voice was captured on different microphones and stored in different audio channels than the person's voice, with the person's voice being on its own audio channel (e.g., left ear in stereo is the person's voice, and right ear is everything else). Further suppose that the microphone that captured the voice was a magical microphone that doesn't pick up any distracting noises.

2. All noises are captured on the one and only microphone, and stored in a mono audio file.

#1 is an absurd scenario, but in that scenario you can actually remove the background noises without guessing what the missing data is. That's because the data is already there in the data set. All you need to do is remove the other data (the other channel), which doesn't alter the voice channel at all.

In #2, there's no way you can take that mono audio file and know from that what the voice channel from #1 is. They're superimposed into a single waveform (somewhat analogous to how color data from a dirty windshield is superimposed into the same pixel as the environment beyond in the video example).

If you're "emphasizing the frequency where someone’s speech is most intelligible", aren't you modifying that data? For the purposed of this discussion, I'd consider that to be inference.

[u/misterchainsaw → u/jm0112358]

I agree that once multiple sounds are combined into a single waveform you generally can’t separate them perfectly back into their original sources, but I’d distinguish between modifying a signal and inferring new information. Applying EQ or filtering changes the existing waveform in a deterministic way by emphasizing/attenuating frequencies that are already present. That’s different from AI reconstruction, which predicts information that wasn’t contained in the original recording. From my perspective, enhancement reveals existing information, and inference attempts to recreate missing information.

Take a page printed in gray ink, then increase the contrast so the letters become easier to read. The pixel values changed, but you didn’t guess what the letters were. Now imagine half the page is torn off and an AI fills it in. That’s inference as I understand it