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Solving the Cocktail Party Problem : Spectral Estimation and Linear Modelling

Keding, Oskar LU (2024) In Licentiate theses in Mathematical Sciences 2024:1.
Abstract
By measuring brain activity, through techniques such as electroencephalography (EEG), it is possible to decode which sound source a person is listening to, called auditory attention decoding (AAD). This can either be done investigating the relation between speech sources and corresponding brain responses over time, or by discrimi-natively estimating directions to which auditory attention is focused. Spectral, temporal and spatial information are all useful and each essential for understanding how the brain processes sounds in a multi-talker scenario. Key challenges with EEG analysis are high levels of noise from various sources, as well as utilizing methods that infer onto the processing happening in the brain. Therefore, the work part of... (More)
By measuring brain activity, through techniques such as electroencephalography (EEG), it is possible to decode which sound source a person is listening to, called auditory attention decoding (AAD). This can either be done investigating the relation between speech sources and corresponding brain responses over time, or by discrimi-natively estimating directions to which auditory attention is focused. Spectral, temporal and spatial information are all useful and each essential for understanding how the brain processes sounds in a multi-talker scenario. Key challenges with EEG analysis are high levels of noise from various sources, as well as utilizing methods that infer onto the processing happening in the brain. Therefore, the work part of this thesis focuses on linear and fairly non-complex methods. This thesis explores spectral estimation based methods and linear modelling methods and their application to AAD. The linear correlation measure of coherence is investigated and improved for use in EEG and AAD, showing that it can differ between attended speech and ignored speech. The commonly applied method of common spatial patterns (CSP) within EEG-data is employed specifically for AAD. We are able to show how different CSP algorithms perform within the field of AAD, and that performance for CSP carries over from decoding auditory attention of individuals with normal hearing compared to individuals with hearing impairment. Independent Component Analysis-based (ICA) methods of removing noise components of EEG data are evaluated for AAD on a dataset with participants hearing impaired. Automatic noise cleaning methods are shown to perform equally as well as the traditional manual method on the given dataset. Finally, a phase estimation technique for transient components based on spectrogram reassignment is developed, which can estimate phase difference of signal components in multi-channel measurements such as EEG. Using the methods described, it is possible to draw interesting conclusions in the field of AAD. However, future work entails further improvement and exploration of useful methods for analysis of the system that is the hearing brain. (Less)
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author
supervisor
organization
alternative title
Att lösa Cocktailparty-problemet : Spektralskattning och linjär modellering
publishing date
type
Thesis
publication status
published
subject
in
Licentiate theses in Mathematical Sciences
volume
2024:1
pages
64 pages
publisher
Centre for Mathematical Sciences, Lund University
ISSN
1404-028X
ISBN
978-91-8104-069-2
978-91-8104-068-5
language
English
LU publication?
yes
id
54cad47a-fad4-49f1-bdcc-d1faadb43f10
date added to LUP
2024-06-03 17:14:07
date last changed
2025-04-04 14:20:49
@misc{54cad47a-fad4-49f1-bdcc-d1faadb43f10,
  abstract     = {{By measuring brain activity, through techniques such as electroencephalography (EEG), it is possible to decode which sound source a person is listening to, called auditory attention decoding (AAD). This can either be done investigating the relation between speech sources and corresponding brain responses over time, or by discrimi-natively estimating directions to which auditory attention is focused. Spectral, temporal and spatial information are all useful and each essential for understanding how the brain processes sounds in a multi-talker scenario. Key challenges with EEG analysis are high levels of noise from various sources, as well as utilizing methods that infer onto the processing happening in the brain. Therefore, the work part of this thesis focuses on linear and fairly non-complex methods. This thesis explores spectral estimation based methods and linear modelling methods and their application to AAD. The linear correlation measure of coherence is investigated and improved for use in EEG and AAD, showing that it can differ between attended speech and ignored speech. The commonly applied method of common spatial patterns (CSP) within EEG-data is employed specifically for AAD. We are able to show how different CSP algorithms perform within the field of AAD, and that performance for CSP carries over from decoding auditory attention of individuals with normal hearing compared to individuals with hearing impairment. Independent Component Analysis-based (ICA) methods of removing noise components of EEG data are evaluated for AAD on a dataset with participants hearing impaired. Automatic noise cleaning methods are shown to perform equally as well as the traditional manual method on the given dataset. Finally, a phase estimation technique for transient components based on spectrogram reassignment is developed, which can estimate phase difference of signal components in multi-channel measurements such as EEG. Using the methods described, it is possible to draw interesting conclusions in the field of AAD. However, future work entails further improvement and exploration of useful methods for analysis of the system that is the hearing brain.}},
  author       = {{Keding, Oskar}},
  isbn         = {{978-91-8104-069-2}},
  issn         = {{1404-028X}},
  language     = {{eng}},
  note         = {{Licentiate Thesis}},
  publisher    = {{Centre for Mathematical Sciences, Lund University}},
  series       = {{Licentiate theses in Mathematical Sciences}},
  title        = {{Solving the Cocktail Party Problem : Spectral Estimation and Linear Modelling}},
  url          = {{https://lup.lub.lu.se/search/files/188264476/LicOskarKeding_LUCRIS.pdf}},
  volume       = {{2024:1}},
  year         = {{2024}},
}