Learning to localize and classify spoken digits: a comparison of two SNN-frameworks
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2022-01-01
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en
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Abstract
The human brain works very efficiently and accurately in tasks that
require localization and recognition of sounds. In the brain, the precise
spike timing of spike trains is used to convey information among
biological neurons. Motivated by this efficient information processing
capability of the brain, it makes sense to try to mimic this process
with the use of spiking neural networks for computational modeling.
In this thesis, two SNN-frameworks are proposed that are able to learn
to localize and classify a set of spoken digits. Both frameworks make
use of Legendre Memory Units and convolution layers, but their overall
structure differs. The first framework uses one neural network to
classify and localize the digits, whereas the second framework uses ten
sub-networks to localize each digit separately. Results show that the
first framework performs better in terms of accuracy and computational
costs but the structure of the second frameworks provides more
flexibility. The described frameworks could potentially be useful in
modeling human speech recognition and localization, but still require
a lot of further research in order to be able to perform in the real world.
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Faculteit der Sociale Wetenschappen