The cocktail party problem and Cochlear Implants, comparing a segmentation and a separation model.
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2019-07-07
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en
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Abstract
In this research two models will be proposed intended to improve main speaker
identi cation in noisy settings, e.g. the Cocktail Party problem, for CI(Cochlear
Implant) users. The aim was not to replicate the attention procedure involved
in the Cocktail Party Problem. The assumption is made that the CI users
performance of speech perception will be improved by attending to the main
speaker instead of the background noise. The goal is to nd which model is
more capable of removing the noise from the signal and thus more able to
solve the simpli ed cocktail party problem for CI users. The rst approach to
the problem uses a model that segments the audio samples while the second
approach uses a deep-clustering neural network to separate the signal from the
noise. The models were tested on two data sets, the rst data set consisted of
samples of two overlapping speakers. The second data set contained samples of 2
to 5 overlapping speakers. The models were only trained on the training subset
of the rst data set but tested on both data sets. The results were evaluated
based on quantitative metrics such as a signal to distortion ratio, a signal to
noise ratio and the short-time objective intelligibility function. The separation
model outperformed the segmentation model signi cantly and is therefore the
better approach to solving the simpli ed cocktail party problem for CI users
based on the results of the quantitative metrics.
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Faculteit der Sociale Wetenschappen