A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

Stefan Kühl; Sebastian Nagel; Tobias Kabzinski; Christiane Antweiler; Peter Jax

ICASSP 2018accepted0 citations

A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

Stefan Kühl, Sebastian Nagel, Tobias Kabzinski, Christiane Antweiler, Peter Jax

Abstract

Research in static and time-variant system identification has brought up a broad variety of identification algorithms. In acoustics, e.g., static measurements of transfer functions are commonly conducted using Inverse Cyclic Convolution (ICC) with Exponential Sweep excitation. Identification and tracking of time-variant systems, however, often employ adaptive filter algorithms, such as the Normalized Least Mean Square (NLMS) algorithm. An interesting implementation variant is the so-called Efficient NLMS (eNLMS) algorithm for arbitrary periodic excitation. ICC and the eNLMS algorithm originate from different fields and have so far evolved independently. This paper bridges the gap using a theoretical analysis of both algorithms to prove that they can be transferred into each other. This understanding provides a joint perspective, such that know-how from both fields can be combined to further optimize the system identification process.

BibTeX

@inproceedings{icassp2018_ajointperspectiv,
  title = {A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution},
  author = {Stefan Kühl and Sebastian Nagel and Tobias Kabzinski and Christiane Antweiler and Peter Jax},
  booktitle = {ICASSP 2018},
  year = {2018}
}