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The aim of the project is related to active control of in-car noise. Basically, an artificially generated sound is superimposed on the unwanted interior noise in order to cancel it out. The canceling signal is obtained by suitable detection and processing of the interior noise so that the unwanted sound perceived by the human observer is considerably reduced. The core of the system consists of an ensemble of “experts' that are specialized in modeling the in-car noise for predefined engine rotation intervals. These are implemented by artificial neural networks, due to their well-known approximation capabilities. Tracking capabilities of the changes occurring in the environment are provided by adaptive weighting of the experts outputs. This action is driven by a discriminator that is able to distinguish between useful sounds (voice, radio, alarm signals) and unwanted noise.

The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.