Acoustic transient identification using neural networks

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Abstract

This paper describes a transient classification paradigm based on adaptive pattern recognition, employing neural networks and the Hausdorff metric. Self-organization is used to provide generalization and rapid throughput while utilizing supervised learning for decision making. The overall approach is to temporally partition acoustic transient signals and study their trajectories through power spectral density space. This method has exhibited encouraging results when applied to a set of acoustic transient signals acquired from recordings of industrial machinery.

Original languageEnglish (US)
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, B.R. Fernandez, J. Ghosh
PublisherASME
Pages769-774
Number of pages6
Volume3
Publication statusPublished - 1993
EventProceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93 - St.Louis, MO, USA
Duration: Nov 14 1993Nov 17 1993

Other

OtherProceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93
CitySt.Louis, MO, USA
Period11/14/9311/17/93

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Cite this

Hemminger, T. L. (1993). Acoustic transient identification using neural networks. In C. H. Dagli, L. I. Burke, B. R. Fernandez, & J. Ghosh (Eds.), Intelligent Engineering Systems Through Artificial Neural Networks (Vol. 3, pp. 769-774). ASME.