<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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].</p></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].

Convolutional Networks Used to Classify Video and Audio Data

Slovak University of Technology in Bratislava

Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics

Faculty of Applied Informatics, Department of Automation and Control Engineering

Research Papers Faculty of Materials Science and Technology Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Convolutional Networks Used to Classify Video and Audio Data"}

Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various...

Convolutional Networks Used to Classify Video and Audio Data

Data publikacji: 16 gru 2019

Zakres stron: 113 - 120

Otrzymano: 26 sie 2019

Przyjęty: 07 paź 2019

DOI: https://doi.org/10.2478/rput-2019-0034

Słowa kluczowe
Convolutional neural network, classification, AlexNet, audio-visual data, deep learning

© 2019 Marcel Nikmon et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Convolutional Networks Used to Classify Video and Audio Data

Data publikacji: 16 gru 2019

Zakres stron: 113 - 120

Otrzymano: 26 sie 2019

Przyjęty: 07 paź 2019

DOI: https://doi.org/10.2478/rput-2019-0034

Słowa kluczoweConvolutional neural network, classification, AlexNet, audio-visual data, deep learning

© 2019 Marcel Nikmon et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Convolutional neural network, classification, AlexNet, audio-visual data, deep learning