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Context-dependent environmental sound monitoring using SOM coupled with LEGION

Damiano Oldoni, Bert De Coensel UGent, Michaël Rademaker, Timothy Van Renterghem UGent, Bernard De Baets UGent and Dick Botteldooren UGent (2010) IEEE International Joint Conference on Neural Networks (IJCNN). p.1413-1420
abstract
Environmental sound measurement networks are increasingly applied for monitoring noise pollution in an urban context. Intelligent measurement nodes offer the opportunity to perform advanced analysis of environmental sound, but trade-offs between cost and functionality still have to be made. When using a tiered architecture, local nodes with limited computing capabilities can be used to detect sound events of potential interest, which are then further analyzed by more powerful nodes. This paper presents a human-mimicking model for detecting rare and conspicuous sound events. Features encoding spectro-temporal irregularities are extracted from the sound, and a Self-Organizing Map (SOM) is used to identify co-occurring features, which most likely belong to a single sound object. Extensive training allows this map to be tuned to the typical sounds that are heard at the microphone location. A Locally Excitatory Globally Inhibitory Oscillator Network (LEGION) is used to group units of the SOM in order to construct distinct sound objects.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
MAP, OSCILLATOR NETWORKS
in
IEEE International Joint Conference on Neural Networks (IJCNN)
issue title
2010 International joint conference on neural networks : IHCNN 2010
pages
1413 - 1420
publisher
IEEE
place of publication
New York, NY, USA
conference name
2010 IEEE World congress on Computational Intelligence (WCCI 2010)
conference location
Barcelona, Spain
conference start
2010-07-18
conference end
2010-07-23
Web of Science type
Proceedings Paper
Web of Science id
000287421401058
ISSN
1098-7576
ISBN
9781424469178
9781424481262
DOI
10.1109/IJCNN.2010.5596977
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1192387
handle
http://hdl.handle.net/1854/LU-1192387
date created
2011-03-21 11:22:07
date last changed
2017-01-02 09:52:34
@inproceedings{1192387,
  abstract     = {Environmental sound measurement networks are increasingly applied for monitoring noise pollution in an urban context. Intelligent measurement nodes offer the opportunity to perform advanced analysis of environmental sound, but trade-offs between cost and functionality still have to be made. When using a tiered architecture, local nodes with limited computing capabilities can be used to detect sound events of potential interest, which are then further analyzed by more powerful nodes. This paper presents a human-mimicking model for detecting rare and conspicuous sound events. Features encoding spectro-temporal irregularities are extracted from the sound, and a Self-Organizing Map (SOM) is used to identify co-occurring features, which most likely belong to a single sound object. Extensive training allows this map to be tuned to the typical sounds that are heard at the microphone location. A Locally Excitatory Globally Inhibitory Oscillator Network (LEGION) is used to group units of the SOM in order to construct distinct sound objects.},
  author       = {Oldoni, Damiano and De Coensel, Bert and Rademaker, Micha{\"e}l and Van Renterghem, Timothy and De Baets, Bernard and Botteldooren, Dick},
  booktitle    = {IEEE International Joint Conference on Neural Networks (IJCNN)},
  isbn         = {9781424469178},
  issn         = {1098-7576},
  keyword      = {MAP,OSCILLATOR NETWORKS},
  language     = {eng},
  location     = {Barcelona, Spain},
  pages        = {1413--1420},
  publisher    = {IEEE},
  title        = {Context-dependent environmental sound monitoring using SOM coupled with LEGION},
  url          = {http://dx.doi.org/10.1109/IJCNN.2010.5596977},
  year         = {2010},
}

Chicago
Oldoni, Damiano, Bert De Coensel, Michaël Rademaker, Timothy Van Renterghem, Bernard De Baets, and Dick Botteldooren. 2010. “Context-dependent Environmental Sound Monitoring Using SOM Coupled with LEGION.” In IEEE International Joint Conference on Neural Networks (IJCNN), 1413–1420. New York, NY, USA: IEEE.
APA
Oldoni, D., De Coensel, B., Rademaker, M., Van Renterghem, T., De Baets, B., & Botteldooren, D. (2010). Context-dependent environmental sound monitoring using SOM coupled with LEGION. IEEE International Joint Conference on Neural Networks (IJCNN) (pp. 1413–1420). Presented at the 2010 IEEE World congress on Computational Intelligence (WCCI 2010), New York, NY, USA: IEEE.
Vancouver
1.
Oldoni D, De Coensel B, Rademaker M, Van Renterghem T, De Baets B, Botteldooren D. Context-dependent environmental sound monitoring using SOM coupled with LEGION. IEEE International Joint Conference on Neural Networks (IJCNN). New York, NY, USA: IEEE; 2010. p. 1413–20.
MLA
Oldoni, Damiano, Bert De Coensel, Michaël Rademaker, et al. “Context-dependent Environmental Sound Monitoring Using SOM Coupled with LEGION.” IEEE International Joint Conference on Neural Networks (IJCNN). New York, NY, USA: IEEE, 2010. 1413–1420. Print.