Acoustic transient identification using neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
StatePublished - 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

Fingerprint

Acoustics
Neural networks
Supervised learning
Power spectral density
Pattern recognition
Machinery
Decision making
Trajectories
Throughput

All Science Journal Classification (ASJC) codes

  • Software

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.
Hemminger, Thomas Lee. / Acoustic transient identification using neural networks. Intelligent Engineering Systems Through Artificial Neural Networks. editor / C.H. Dagli ; L.I. Burke ; B.R. Fernandez ; J. Ghosh. Vol. 3 ASME, 1993. pp. 769-774
@inproceedings{09dbbff4fb8a47be8e298e0fc8911c70,
title = "Acoustic transient identification using neural networks",
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.",
author = "Hemminger, {Thomas Lee}",
year = "1993",
language = "English (US)",
volume = "3",
pages = "769--774",
editor = "C.H. Dagli and L.I. Burke and B.R. Fernandez and J. Ghosh",
booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks",
publisher = "ASME",

}

Hemminger, TL 1993, Acoustic transient identification using neural networks. in CH Dagli, LI Burke, BR Fernandez & J Ghosh (eds), Intelligent Engineering Systems Through Artificial Neural Networks. vol. 3, ASME, pp. 769-774, Proceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93, St.Louis, MO, USA, 11/14/93.

Acoustic transient identification using neural networks. / Hemminger, Thomas Lee.

Intelligent Engineering Systems Through Artificial Neural Networks. ed. / C.H. Dagli; L.I. Burke; B.R. Fernandez; J. Ghosh. Vol. 3 ASME, 1993. p. 769-774.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Acoustic transient identification using neural networks

AU - Hemminger, Thomas Lee

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0027898091&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027898091&partnerID=8YFLogxK

M3 - Conference contribution

VL - 3

SP - 769

EP - 774

BT - Intelligent Engineering Systems Through Artificial Neural Networks

A2 - Dagli, C.H.

A2 - Burke, L.I.

A2 - Fernandez, B.R.

A2 - Ghosh, J.

PB - ASME

ER -

Hemminger TL. Acoustic transient identification using neural networks. In Dagli CH, Burke LI, Fernandez BR, Ghosh J, editors, Intelligent Engineering Systems Through Artificial Neural Networks. Vol. 3. ASME. 1993. p. 769-774