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

Authors:

Kühl, S. ,  Nagel, S. ,  Kabzinski, T. ,  Antweiler, C. ,  Jax, P.

Book Title:

Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Organization:

IEEE

Publisher:

IEEE

Pages:

p.p. 406-410

Date:

Apr. 2018

ISBN:

978-1-53864-658-8

DOI:

10.1109/ICASSP.2018.8461341

Language:

English

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.

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