Download Feature design for the classification of audio effect units by input/output measurements 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.
Download A Differentiable Digital Moog Filter For Machine Learning Applications In this project, a digital ladder filter has been investigated and expanded. This structure is a simplified digital analog model of the well known analog Moog ladder filter. The goal of this paper is to derive the differentiation expressions of this filter with respect to its control parameters in order to integrate it in machine learning systems. The derivation of the backpropagation method is described in this work, it can be generalized to a Moog filter or a similar filter having any number of stages. Subsequently, the example of an adaptive Moog filter is provided. Finally, a machine learning application example is shown where the filter is integrated in a deep learning framework.