Noise Statistics Estimation based on adaptive filtering

Bachelor thesis (2019)

Authors

W.W.W. Sewnarain Electrical Engineering, Mathematics and Computer Science
M. Rijkeboer Electrical Engineering, Mathematics and Computer Science

Contributors

R.C. Hendriks Signal Processing Systems - EEMCS (supervisor 1)
A. Koutrouvelis Signal Processing Systems - EEMCS (supervisor 1)
I.E. Lager Electrical Engineering Education - EEMCS (supervisor 2)
Jorge Martinez Electrical Engineering Education - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2019 Winay Sewnarain, Mats Rijkeboer
Adaptive Filtering Wiener filter Noise estimation Least mean square adaptive algorithm
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Published Date

02-07-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

At virtually every public venue, announcements for visitors are made via a public addressing system. It is important that announcements transmitted by means of a public addressing system are not only audible, but also well understood by the general public. This thesis covers one of three subsystems required to make
a product that is capable of improving intelligibility of speech based on noise statistics in a room. Moreover, these statistics allow for an automatic volume control in order for listeners to experience an improved audio level. Therefore, this thesis aims at estimating the noise statistics in real time, while prior knowledge on the distorting announcement signal is available. Consequently, the concept of adaptive filtering deems suiting and is extensively studied. In order to meet the real time processing constraint, members of least mean squares algorithms are examined. Therefore, the method of steepest decent is covered, leading to the expounding of the traditional least mean squares (LMS) algorithm and the normalized LMS (NLMS) algorithm.
By assessment of the algorithms regarding different step sizes and filter lengths, the NLMS algorithm is shown to be superior in terms of faster convergence speed and better stability characteristics considering similar conditions for both algorithms. Subsequently, the results show that the NLMS is able to estimate the
noise in noise-to-signal ratio’s higher than -15dB. Also, its low complexity allows it to be suitable for real time applications, hence meeting the requirements.