Virtual analog modeling is an important field of digital audio signal processing. It allows to recreate the tonal characteristics of real-world sound sources or to impress the specific sound of a certain analog device upon a digital signal on a software basis. Automatic virtual analog modeling using black-box system identification based on input/output (I/O) measurements is an emerging approach, which can be greatly enhanced by specific pre-processing methods suggesting the best-fitting model to be optimized in the actual identification process. In this work, several features based on specific test signals are presented allowing to categorize instrument effect units into classes of effects, like distortion, compression, modulations and similar categories. The categorization of analog effect units is especially challenging due to the wide variety of these effects. For each device, I/O measurements are performed and a set of features is calculated to allow the classification. The features are computed for several effect units to evaluate their applicability using a basic classifier based on pattern matching.

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Virtual analog modeling is the process of digitally recreating an analog device. This study focuses on analog distortion pedals for guitarists, which are categorized as stompboxes, because the musician turns them on and off by stepping on the switch. While some of the current digital models of distortion effects are circuit-based, this study uses a signal-based approach to identify the device under test (DUT). An algorithm to identify any distortion effect pedal in any given setting by input-output (I/O) measurements is proposed. A parametric block-oriented Wiener-Hammerstein model for distortion effects and the corresponding iterative error minimization procedure are introduced. The algorithm is implemented in Matlab and uses the Levenberg-Marquardt minimization procedure with boundaries for the parameters.

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