distorsionsmodeller

distorsionsmodeller is a framework for constructing, analyzing, and applying distortion models to signals, with particular emphasis on audio. It provides a common vocabulary and toolchain for representing both linear and nonlinear distortions and their memory effects, enabling researchers and engineers to simulate, identify, and compensate distortions in synthesis, measurement, and restoration tasks. The framework supports a range of model classes, from memoryless (static) nonlinearities to dynamic systems that capture temporal behavior.

Model types are typically categorized as memoryless polynomial models, piecewise linear approximations, and dynamic models such as Volterra series, Wiener or Hammerstein structures, autoregressive nonlinear models, and modern data-driven approaches like neural networks. The architecture is modular: separate components handle data acquisition, model specification, parameter estimation, model validation, and signal synthesis or distortion emulation. Estimation methods include least squares, maximum likelihood, and gradient-based optimization, often combined with regularization to prevent overfitting. Validation uses objective metrics and perceptual relevance tests.

Common applications include emulating guitar amplifiers and effects pedals, cabinet and loudspeaker distortion modeling, audio restoration and quality control, and psychoacoustic research into distortion perception. The tool is often integrated into digital audio workstations or used in research pipelines via libraries and plug-ins. While powerful, distorsionsmodeller remains an approximation of real devices, and model accuracy depends on representative data and appropriate choice of model class.