Self-organizing map to filter acoustic mapping survey in noise pollution analysis

In this paper the authors propose a neural approach to filter the data provided by acoustic measurements. It is based on the use of a Kohonen self-organizing map network which, in the learning phase receives correct acoustic measurements. The Kohonen neural network learning on the basis of this set of measurements would allow the network to be used as a filter. Having received a set of acoustic measurements in input, it would be able, in the production phase, to discard any acoustic measurements which were insignificant or affected by errors.