SignalTrain: Profiling Audio Compressors with Deep Neural Networks
This work addresses the challenge of profiling audio effects like compressors for audio engineers and researchers, but it is incremental as it builds on existing data-driven methods with specific improvements.
The paper tackled the problem of predicting the behavior of non-linear audio compressors by learning a mapping from unprocessed to processed audio using a deep auto-encoder conditioned on time-domain samples and control parameters. Results showed that primary functional and auditory characteristics were captured, but audible noise remained, indicating need for further investigation before real-world application.
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. To that aim, 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 study, we focus on the offline profiling of two dynamic range compression audio effects, one software-based and the other analog. Compressors were chosen because they are a widely used and important set of effects and because their parameterized nonlinear time-dependent nature makes them a challenging problem for a system aiming to profile "general" audio effects. Results from our experimental procedure show that the primary functional and auditory 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.