A multiple sound source recognition system using pulsed neuron model with short term synaptic depression

Authors:
Kaname Iwasa;Mauricio Kugler;Susumu Kuroyanagi;Akira Iwata
Affiliations:
Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Nagoya, Japan;Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Nagoya, Japan;Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Nagoya, Japan;Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Nagoya, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

Citing 2
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Signal Processing
Pulsed Neural Networks

Pulsed Neural Networks

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Abstract

Many applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. However, one of the main difficulties of such kind of system is the natural presence of mixed sound sources in real environments. This paper proposes a pulsed neural network based system for extraction and recognition of objective sound sources from background sound source. The system uses the short term depression, that implements by the weight's decay in the output layer and changing the weight by frequency component in the competitive learning network. Experimental results show that objective sounds could be successfully extracted and recognized.