SignalTrain: Profiling Audio Compressors with Deep Neural Networks

28 May 2019Scott H. HawleyBenjamin ColburnStylianos I. Mimilakis

In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect"). Our objective is to learn a mapping function that maps the unprocessed audio to the processed by the audio effect to be profiled, using time-domain samples... (read more)

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