Comparing audio classification using a convolutional- and a recurrent neural network
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2023-01-27
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en
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This thesis project will try to evaluate the differences between a CNN and an RNN when
used for an audio classification task. A CNN is known to perform well at classifying images,
which audio fragments can easily be converted to. Alternatively, an RNN is known
to be able to capture (long) time dependencies, which are inherently present in sound
as sound is a signal over time. These different advantages may or may not yield positive
effects when classifying audio. Differences between the results of the two strategies
are interesting to be evaluated and could pertain to the essence of the strategies themselves.
The findings can be used to draw conclusions about whether one strategy might
be preferable over the other in an audio classification task related to urban sounds.
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Faculteit der Sociale Wetenschappen