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Decoding Auditory Attention from Multivariate Neural Data using Cepstral Analysis

Mendoza, Carlos Francisco and Segar, Andrew (2018) MASM01 20181
Mathematical Statistics
Abstract
Very little is known about the remarkable ability of humans to separate a single sound source from a dense mixture of sound sources in a crowded background, known as the cocktail-party scenario. Better understanding could lead to a breakthrough for the next-generation of hearing aids to have the ability to be cognitively controlled. A key finding in the field is that human cortical activity has been shown to follow the speech envelope. However, in these
experimental results, the correlation coefficients between the EEG and speech envelope are very low, on the order of r = 0.1-0.2. Also, classification rates are not yet 100%. The aim of this project is to investigate whether cepstral analysis can be used as a more robust mapping between... (More)
Very little is known about the remarkable ability of humans to separate a single sound source from a dense mixture of sound sources in a crowded background, known as the cocktail-party scenario. Better understanding could lead to a breakthrough for the next-generation of hearing aids to have the ability to be cognitively controlled. A key finding in the field is that human cortical activity has been shown to follow the speech envelope. However, in these
experimental results, the correlation coefficients between the EEG and speech envelope are very low, on the order of r = 0.1-0.2. Also, classification rates are not yet 100%. The aim of this project is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary results show correlations on the order of r > 0.5. This thesis will give a insight into the method we are developing, our current
results, and the expected future results and applications in hearing aids. (Less)
Please use this url to cite or link to this publication:
author
Mendoza, Carlos Francisco and Segar, Andrew
supervisor
organization
course
MASM01 20181
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
8952753
date added to LUP
2018-06-25 14:25:07
date last changed
2018-06-25 14:25:07
@misc{8952753,
  abstract     = {{Very little is known about the remarkable ability of humans to separate a single sound source from a dense mixture of sound sources in a crowded background, known as the cocktail-party scenario. Better understanding could lead to a breakthrough for the next-generation of hearing aids to have the ability to be cognitively controlled. A key finding in the field is that human cortical activity has been shown to follow the speech envelope. However, in these
experimental results, the correlation coefficients between the EEG and speech envelope are very low, on the order of r = 0.1-0.2. Also, classification rates are not yet 100%. The aim of this project is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary results show correlations on the order of r > 0.5. This thesis will give a insight into the method we are developing, our current
results, and the expected future results and applications in hearing aids.}},
  author       = {{Mendoza, Carlos Francisco and Segar, Andrew}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Decoding Auditory Attention from Multivariate Neural Data using Cepstral Analysis}},
  year         = {{2018}},
}