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Context-dependent environmental sound monitoring using SOM coupled with LEGION

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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.
Keywords
MAP, OSCILLATOR NETWORKS

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Citation

Please use this url to cite or link to this publication:

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.
@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},
}

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