Input-output linearization is a technique for compensating nonlinear distortion in loudspeakers. To apply it to complex loudspeaker models, we describe an end-to-end framework for estimating model parameters from data; deriving the linearizing control laws using automatic differentiation. The parameter estimation approach combines frequency-domain linear parameter estimation with a time-domain prediction-error method for the nonlinear parameters. The linearization approach supports non-linear reference systems; stabilization of the control law using trajectory tracking. We implement the framework in dynax, an open-source Python package based on JAX,; validate it experimentally as a feed-forward controller on a closed-box loudspeaker. Results demonstrate validation errors of 1--5\,\% NRMSE; total harmonic distortion reductions of 6--12\,dB. The framework enables researchers ; engineers to rapidly prototype; validate complex loudspeaker models for distortion compensation without manual symbolic derivations.
My interest are loudspeakers (measurements, modelling, (nonlinear) parameter estimation, nonlinear compensation. Active noise control, indoor and outdoor sound field control
