Noise Statistics Estimation based on adaptive filtering

Title

Noise Statistics Estimation based on adaptive filtering

 Author

Sewnarain, Winay (TU Delft Electrical Engineering, Mathematics and Computer Science)
Rijkeboer, Mats (TU Delft Electrical Engineering, Mathematics and Computer Science)

Contributor

Hendriks, Richard (mentor)
Koutrouvelis, Andreas (mentor)
Lager, Ioan (graduation committee)
Martinez Castaneda, Jorge (graduation committee)

Degree granting institution

Delft University of Technology

 Programme

Electrical Engineering

 Project

Intelligibility-Enhancing Automatic Volume Control System

 Date

2019-07-02

 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.

 Subject

Adaptive Filtering
Wiener filter
Noise estimation
Least mean square adaptive algorithm

 To reference this document use:

http://resolver.tudelft.nl/uuid:9011b256-576e-48e1-85db-e8bb9eeb6b88

 Part of collection

Student theses

 Document type

bachelor thesis

 Rights

© 2019 Winay Sewnarain, Mats Rijkeboer
Files
PDF BAP_Noise_Statistics_Estimation.pdf 1.3 MB
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