Abstract
We present a technique to iteratively optimize poles of a recursive digital filter in parallel form. Only exposing the poles as the variables to optimize, we employ a linearly constrained gradient descent routine in which the numerical estimation of the error gradient involves first obtaining the zeros by projecting the target response over a basis of responses defined by the pole positions at a given step. Example fits are presented for exponentially decaying white noise and measured violin radiativity filters.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.