Convolutional Networks Used to Classify Video and Audio Data

Nikmon Marcel (), Budjač Roman (), Kuchár Daniel (), Schreiber Peter () and Janáčová Dagmar ()
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Nikmon Marcel: Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu 25, 917 24Trnava, Slovak Republic
Budjač Roman: Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu 25, 917 24Trnava, Slovak Republic
Kuchár Daniel: Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu 25, 917 24Trnava, Slovak Republic
Schreiber Peter: Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu 25, 917 24Trnava, Slovak Republic
Janáčová Dagmar: Tomas Bata University of Zlin, Faculty of Applied Informatics, Department of Automation and Control Engineering, Nad Stráněmi 4511, 760 05Zlín, Czech Republic

Research Papers Faculty of Materials Science and Technology Slovak University of Technology, 2019, vol. 27, issue 45, 113-120

Abstract: Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input data. However, there are also comparisons of other neural network architectures, and we also show the results of training on small and larger datasets. The paper comprises description of eight different kinds of experiments. Several training sessions were conducted in each experiment with different aspects that were monitored. The focus was put on the effect of batch size on the accuracy of deep learning, including many other parameters that affect deep learning [1].

Keywords: Convolutional neural network; classification; AlexNet; audio-visual data; deep learning (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:repfms:v:27:y:2019:i:45:p:113-120:n:16

DOI: 10.2478/rput-2019-0034

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