I’m working on a project in digital signal processing (DSP). To put it simply, I’m trying to create a model that can mimic effects applied to a musical signal. For instance, the domain, X, is clean guitar signal and the range, f(X), is the same guitar signal under some effect, f. This function f could be distortion, chorus, delay, reverb, etc…. All that matters is that f maps a clean guitar signal to some altered signal f: X -> f(X).

My modeling task is to model f without know what the function, f, is exactly. I’ve successfully trained a LSTM model to mimic the effect of chorus and I’m sure I could train a model to model other effects such as delay or reverb.

My issue is that the predictions on new signals take so long. A couple seconds of audio can end up taking minutes to predict. I currently make predictions on a sample-by-sample level (an average sampling rate is 22,000 per second). I’m trying to find a solution that could make predictions (i.e. alter the input signal) in near real-time. Is there a specific type of modeling I can try that will results in a model that can make fast predictions? Or do you have any ideas on how to take a model and allow it to make near real-time predictions? The thing is that in reality these affects can be applied so quickly because they are simple transformations. For instance, chorus is just a result of phase-shifting a signal and adding it back to the original signal. Any help is appreciated. Thanks!