Mikrofonik & NexaFlow
NexaFlow
Hey, I was thinking about how we could use AI to map out the perfect mic placement grid for a recording session—like a digital assistant that tells you exactly where to put the mic for that golden sound. Do you think a neural net could beat our traditional rule‑of‑thirds approach?
Mikrofonik
Honestly, I’d be wary of letting a neural net replace the tried‑and‑true 45‑degree angle and 3‑inch rule‑of‑thirds. A model can learn patterns, but it can’t account for the room’s bass traps, the mic’s polar pattern quirks, or that one acoustic patch that makes everything sound like a catwalk. It might give you a starting grid, but you’ll still need to tweak it until the phantom hum disappears. If you want the AI to be your assistant, let it suggest, not decide.
NexaFlow
I hear you, and that’s a solid point—machines can suggest, but the human touch catches those quirks. Maybe the AI could just give a rough map, and then you layer in the room‑specific tweaks. That way it stays a helper, not a replacement, and we still get that “hand‑crafted” vibe we love.
Mikrofonik
That sounds like the right balance—let the AI plot the skeleton and then you add the flesh. Just remember to keep one eye on the mic’s polar curve; a flat‑shot can make a tweeter sound like a kazoo if you’re not careful. Trust the data, but let the analog instinct make the final call.
NexaFlow
Exactly—data gives us the outline, intuition gives us the curve. If that tweeter starts sounding like a kazoo, we just tweak the polar curve and trust the hands that know the room. It's all about blending precision with that instinctive feel.
Mikrofonik
Love that balance—precision for the map, instinct for the tweak. Just don’t forget the tweeter’s sweet spot; a little push on the polar curve and the kazoo gets a little less kazoo. Keep the hands, keep the data, and the room will thank you.