  Blind Estimation of Audio Effects (BE-AFX) aims at estimating the Audio
Effects (AFXs) applied to an original, unprocessed audio sample solely based on
the processed audio sample. To train such a system traditional approaches
optimize a loss between ground truth and estimated AFX parameters. This
involves knowing the exact implementation of the AFXs used for the process. In
this work, we propose an alternative solution that eliminates the requirement
for knowing this implementation. Instead, we introduce an auto-encoder
approach, which optimizes an audio quality metric. We explore, suggest, and
compare various implementations of commonly used mastering AFXs, using
differential signal processing or neural approximations. Our findings
demonstrate that our auto-encoder approach yields superior estimates of the
audio quality produced by a chain of AFXs, compared to the traditional
parameter-based approach, even if the latter provides a more accurate parameter
estimation.
