Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks

Constantin Rieder; Markus Germann; Samuel Mezger; Klaus Peter Scherer

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Paper

Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks

Topics: Convolutional Neural Networks (CNN); Image Classification; Image Retrieval; Learning Deep Generative Models; Object Detection; Recurrent Neural Network (RNN)

In Proceedings of the 1st International Conference on Deep Learning Theory and Applications DeLTA - Volume 1, 84-88, 2020

Authors: Constantin Rieder ; Markus Germann ; Samuel Mezger and Klaus Peter Scherer

Affiliation: Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, Germany

Keyword(s): Deep Learning, Sound Analysis, Information Systems, Intelligent Assistance.

Abstract: In many intelligent technical assistance systems (especially diagnostics), the sound classification is a significant and useful input for intelligent diagnostics. A high performance classification of the heterogeneous sounds of any mechanical components can support the diagnostic experts with a lot of information. Classical pattern recognition methods fail because of the complex features and the heterogeneous state noise. Because of no explicit human knowledge about the characteristic representation of the classes, classical feature generation is impossible. A new approach by generation of a concept for neural networks and realization by especially convolutional networks shows the power of technical sound classification methods. After the concept finding a parametrized network model is devised and realized. First results show the power of the RNNs and CNNs. Dependent on the parametrized configuration of the net architecture and the training sets an enhancement of the sound event clas sification is possible. (More)

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Paper citation in several formats:

Rieder, C.; Germann, M.; Mezger, S. and Scherer, K. (2020). Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks. In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-441-1, SciTePress, pages 84-88. DOI: 10.5220/0009874400840088

@conference{delta20,
author={Constantin Rieder. and Markus Germann. and Samuel Mezger. and Klaus Peter Scherer.},
title={Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA},
year={2020},
pages={84-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009874400840088},
isbn={978-989-758-441-1},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA
TI - Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks
SN - 978-989-758-441-1
AU - Rieder, C.
AU - Germann, M.
AU - Mezger, S.
AU - Scherer, K.
PY - 2020
SP - 84
EP - 88
DO - 10.5220/0009874400840088
PB - SciTePress