We present a data-driven approach for predicting the behavior of (i.e., profiling) a given parameterized, non-linear time-dependent audio signal processing effect. Our objective is to learn a mapping function that maps the unprocessed audio to the processed, using time-domain samples. We employ a deep auto-encoder model that is conditioned on both time-domain samples and the control parameters of the target audio effect. As a test-case, we focus on the offline profiling of two dynamic range compressors, one software-based and the other analog. Our results show that the primary characteristics of the compressors can be captured, however there is still sufficient audible noise to merit further investigation before such methods are applied to real-world audio processing workflows.
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