In an ever-expanding and competitive market, commercial digital audio effects have significant constraints. Their computation load must be reduced so that they can be operable in real-time. They must be easily controllable through parameters that should be scarce and relate to clear features. They must be robust and safe for large combinations of inputs and controls to allow for user creativity as well. Effects based on existing systems (acoustic or electronic devices) must in addition sound realistic and capture expected idiosyncrasies.

For this last category of effects, a full physical model is not always available or even desirable, as it can be both too complex to run and be used efficiently. In this talk, we explore grey-box approaches that combine strong physically-based priors and identification from measurement data. The priors impose a model structure that preserves fundamental properties such as passivity and dissipativity, while measurements allow to bridge possible gaps in the model. This produces reduced, macroscopic, power-balanced models of complex physical systems that can be fitted to data, and result in numerically stable simulations. This approach is illustrated on real electronic components and circuits, with audio demonstrations of the corresponding effects to complete the presentation.