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Auditory Classification of Carsby Deep Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis explores the challenge of using deep neural networks to classify traits incars through sound recognition. These traits could include type of engine, model, or manufacturer of the car. The problem was approached by creating three different neural networks and evaluating their performance in classifying sounds of three different cars. The top scoring neural network achieved an accuracy of 61 percent, which is far from reaching the standard accuracy of modern speech recognition systems. The results do, however, show that there are some tendencies to the data that neural networks can learn. If the methods and networks presented in this report are further built upon, a greater classification performance may be achieved.

Place, publisher, year, edition, pages
2018. , p. 74
Series
UPTEC IT, ISSN 1401-5749 ; 18010
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-355673OAI: oai:DiVA.org:uu-355673DiVA, id: diva2:1230305
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-07-03Bibliographically approved

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