Intelligent ventilator positioning via acoustic feedback

A possible solution for the conflict between natural ventilation and passive noise control is presented by introducing a knowledge-based noise classification and position control system. The system controls the position of windows or ventilators as it recognizes an approaching aircraft or noisy vehicle. The level of intrusive transportation noise into the building can be controlled by activating a knowledge-based controller using acoustic feedback. Fuzzy logic and rule-based algorithms are implemented in three major steps: training, source classification and control. To identify an approaching noise source, estimated parameters of a second order autoregressive model AR(2), the dominant peaks of short-time Fourier transform (STFT) and the cross-correlation between output data from indoor and outdoor microphones, are used. If the dominant indoor noise is from transportation sources, the noise level is used as acoustic feedback in a rule-based control loop. Training software is employed to update the detection and classification thresholds and to generate the triangle membership functions with three linguistic variables for decision making and control scenario