I have a firm grasp on genetic programming, PSO, trees and search strategies like Minimax, etc. I haven't yet got to the point of learning about neural networks beyond the absolute basics.
A project I was humoring involved audio signal processing. I want to know viable AI strategies to implement what I was thinking.
Consider two musicians with two different instruments. The first is playing a violin and the second a guitar. Even if both played the same note, they sound different because of the shape of the instrument, perhaps string gauge, even technique. I wonder of possible ways to make the guitar sound like the violin or vice versa.
Given an audio recording of a violin playing a scale, then a live performance of a guitar playing the same scale, what AI methods could shape the output sound of the guitar to be close to the sound of the violin?
I think it would boil down a lot to equalization. Different frequencies can be boosted or diminished, but how do you find the perfect equalization settings? Hence the question.
What AI strategies would be well suited to this, specifically something that can be done on the spot? In my experience, tree searches take time and might be inappropriate. Genetic algorithms may be better. What about neural networks?
I already have an obvious heuristic: how closely the sound waves match. I just wonder about speed.