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Blind Estimation of Audio Effects (BE-AFX) aims at estimating the AudioEffects (AFXs) applied to an original, unprocessed audio sample solely based onthe processed audio sample. To train such a system traditional approachesoptimize a loss between ground truth and estimated AFX parameters. Thisinvolves knowing the exact implementation of the AFXs used for the process. Inthis work, we propose an alternative solution that eliminates the requirementfor knowing this implementation. Instead, we introduce an auto-encoderapproach, which optimizes an audio quality metric. We explore, suggest, andcompare various implementations of commonly used mastering AFXs, usingdifferential signal processing or neural approximations. Our findingsdemonstrate that our auto-encoder approach yields superior estimates of theaudio quality produced by a chain of AFXs, compared to the traditionalparameter-based approach, even if the latter provides a more accurate parameterestimation.

Blind estimation of audio effects using an auto-encoder approach and differentiable signal processing