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The necessity to meet ongoing needs of industry, considering theoretical progress achievements and availability of cost-effective equipment, has encouraged numerous researchers to investigate the application of monitoring systems. In this paper the sound localization is implemented to find the impact position on the surface of a plate. As an experimental example the sound caused by ball impact on a ping pong table is used. For this purpose, a database is gathered. These sound's signals were recorded 25 times at 5 different points along the length of the table by a low cost microphone, attached to the surface. In the proposed method, first the data related to the ball impacts are detected and isolated from the whole pc recorded signals sent by the microphone. Then, the above 125 impacts are clustered based on the impact point locations, using a 4 dimensional space feature extracted from statistical signal moments. Furthermore in order to specify sound localization, a second space feature based on energy of wavelet transform coefficient signals was extracted. Ultimately for clustering the impact point locations, an artificial neural network was designed and applied to the above data. The results show average values of sensitivity Se=91.20% and positive predictivity P+=91.18%. Also, sensitivity Se=91.97% and positive predictivity P+‌=‌93.45%, correspondingly for impact localization.

Keywords: Sound Localization, Low cost sensor, Feature Extraction

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