Nonlinear distortion analysis using digital higher-order coherence spectra

A digital spectral method of evaluating second-order distortion of a nonlinear system, which can be represented by Volterra kernels up to second-order and which is subjected to a random noise input, is discussed. A fundamental assumption that the input possesses Gaussian statistics is investigated. Deleterious effects and misleading results of assuming that the input is Gaussian when in fact it is not are demonstrated. The proposed approach is utilized to model, identify, and quantify the linear response and second-order distortion products of a loudspeaker in the low-frequency band by using higher-order coherence spectra