Pipelined Volterra filter

Banat, Mohammad M.

doi:10.1049/el:19920808

Cited by 9 publications

(2 citation statements)

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“…Using a filter multiply-accumulate (MAC) operations for the filter bank operations. Many attempts have been made to reduce this number, by a proper combination of the operations, either in time [42,43] or in frequency domain [44][45][46]. Regardless of these techniques, Table 9 reports the number of MAC required by each proposed model per each sample and the million of MAC per second (MMACS), by using a filter bank implementation.…”

Section: Discussionmentioning

confidence: 99%

Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method

Orcioni¹,

Terenzi²,

Cecchi³

et al. 2018

J. Audio Eng. Soc.

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The multiple-variance method is a cross-correlation method that exploits input signals with different powers for the identification of a nonlinear system by means of the Volterra series. It overcomes the problem of the locality of the solution of traditional nonlinear identification methods, based on mean square error minimization or cross-correlation, that well approximate the system only for inputs that have approximately the same power of the identification signal. The multiple-variance method permits to improve the performance of models of systems that have inputs with high dynamic, like audio amplifiers. This method is used, for the first time, to identify three different tube amplifiers. The method is applied to a novel reduced Volterra model that allows to overcome the problem of the very large number of coefficients required by the Volterra series by choosing only a proper subset of elements from each kernel. Eventually, the multiple-variance methodology is applied to different real audio tube devices demonstrating the effectiveness of the proposed approach in terms of system identification and computational complexity.

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Section: Discussionmentioning

confidence: 99%

Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method

Orcioni¹,

Terenzi²,

Cecchi³

et al. 2018

J. Audio Eng. Soc.

View full text Add to dashboard Cite

show abstract

“…From Eqs. (18) and (27), we can see that the nonlinear updating processes of PAVF and HPAVF(1) are related to the input signal of each module. Because of the output of HPAVF (Eq.…”

Section: Examplementioning

confidence: 99%