Environmental Noise Source Classification Using Neural Networks

Author

Barkana, Buket D. ; Saricicek, Inci

Author_Institution

Dept. of Electr. Eng., Univ. of Bridgeport, Bridgeport, CT, USA

fYear

2010

fDate

12-14 April 2010

Firstpage

259

Lastpage

263

Abstract

Neural networks have been applied to many interesting problems in different areas including noise identification/recognition. With this study, we studied noise classification using artificial neural networks (ANN). Three commonly encountered non-stationary noise sources are chosen to recognize. These are highway, subway and airport. Time-domain based feature parameters are used. While one-phase ANN classifier achieving 54% accuracy, two-phase ANN classifier achieved 83-89% accuracy rates.

Keywords

acoustic signal processing; neural nets; noise (working environment); pattern classification; ANN classifier; airport noise; artificial neural networks; environmental noise; highway noise; noise source classification; nonstationary noise sources; subway noise; time-domain based feature parameters; Acoustic noise; Artificial neural networks; Background noise; Classification tree analysis; Electronic mail; Hidden Markov models; Neural networks; Pattern recognition; Speech; Working environment noise; ACF-based feature parameter; Neural Networks (ANN); environmental noise classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology: New Generations (ITNG), 2010 Seventh International Conference on

Conference_Location

Las Vegas, NV

Print_ISBN

978-1-4244-6270-4

Type

conf

DOI

10.1109/ITNG.2010.118

Filename

5501721