The paper discusses a multiple variance methodology for measuring the impulse response for small signals of a mildly nonlinear system, i.e., the first-order kernel of a Volterra model. It is shown with theory that using multiple variance inputs and any linear impulse response measurement method, it is possible to accurately estimate the first-order kernel by applying a polynomial interpolation. The values of the input gains that minimize the influence of noise are also determined. The experimental results, considering an emulated scenario, show how the proposed method can be effectively used to accurately estimate the room impulse response in case of nonlinearities in the measurement system.
Polynomial Multiple Variance Impulse Response Measurement
Orcioni S.Ultimo
2022-01-01
Abstract
The paper discusses a multiple variance methodology for measuring the impulse response for small signals of a mildly nonlinear system, i.e., the first-order kernel of a Volterra model. It is shown with theory that using multiple variance inputs and any linear impulse response measurement method, it is possible to accurately estimate the first-order kernel by applying a polynomial interpolation. The values of the input gains that minimize the influence of noise are also determined. The experimental results, considering an emulated scenario, show how the proposed method can be effectively used to accurately estimate the room impulse response in case of nonlinearities in the measurement system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
