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[P] Anyone Know of Methods for Fast Audio Prediction?

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!

submitted by /u/knestleknox
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.