In addition to the degradation of the medium itself, another issue in the world of audio preservation/restoration is the degradation of the playback equipment. Many hardware devices are out of production and use obsolete parts that are difficult to find and maintain - especially for smaller institutions. We’ve looked into some digital modeling of vintage hardware or hardware processes to try address these issues. A significant part of this work was the creation of new datasets to allow researchers to evaluate their models more accurately.

Our first paper in this area was on the Solid State Logic (SSL) FX G-384 Bus Compressor, which was generously donated to the University of Rochester by the estate of Ric Ocasek. The quantity that defines the behavior of a dynamic range compressor is the time-varying gain applied to the signal as a function of the input level. However, models of these devices are typically evaluated using proxy metrics because isolating the gain reduction signal from the audio input–output data included in existing datasets creates an ill-conditioned inverse problem. It is unclear how accurately these metrics describe the behavior the model is tasked with emulating, particularly as waveform-based metrics can be influenced by secondary effects introduced by analog processing and capture, even when those effects are inaudible. We investigate a method of evaluation in which the gain-reduction signal produced by a model is measured directly against a gain-reduction control voltage signal produced by the hardware. To evaluate the efficacy of this metric as a learning objective, a gray-box model is trained using loss computed directly over the gain control signals alongside two models trained using common proxy losses. The models trained using proxy losses did not achieve parity with models trained directly on the gain control signal when evaluated with respect to the underlying control trajectory, and the waveform-domain metrics assigned similar errors to models that were clearly separated by the direct metric. To facilitate further exploration of this method of evaluation, we created a Solid State Logic bus compressor dataset that includes the gain control voltage signal captured alongside the audio output. A more detailed dive into the electronics of this particular unit is in preparation.