Acoustic transient identification using neural networks

Thomas Lee Hemminger

Acoustic transient identification using neural networks

Thomas Lee Hemminger

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Intelligent Engineering Systems Through Artificial Neural Networks
Editors	C.H. Dagli, L.I. Burke, B.R. Fernandez, J. Ghosh
Publisher	ASME
Pages	769-774
Number of pages	6
Volume	3
State	Published - 1993
Event	Proceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93 - St.Louis, MO, USA Duration: Nov 14 1993 → Nov 17 1993

Other

Other	Proceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93
City	St.Louis, MO, USA
Period	11/14/93 → 11/17/93

All Science Journal Classification (ASJC) codes

Software

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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",

note = "Proceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93 ; Conference date: 14-11-1993 Through 17-11-1993",

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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

AN - SCOPUS:0027898091

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

T2 - Proceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93

Y2 - 14 November 1993 through 17 November 1993

ER -

Acoustic transient identification using neural networks

Abstract

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All Science Journal Classification (ASJC) codes

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