Static and dynamic signal processing methods for practical use based on some extension of Gaussian probability in stochastic evaluation of room EM and acoustic environment

In this paper, we have proposed contrastively static and dynamic type two practical and methodological approaches in order to overcome the complexity of actual electromagnetic (abbr. EM) and sound environments. One is an extended regression analysis of static type intended for employing a standard Gaussian distribution for evaluating mutual relationships between sound and light leaked from a video display terminal (abbr. VDT) in a room, and the other is an establishment of Kalman’s filtering algorithm of dynamic type with help of an equivalence transformation toward the standard Gaussian distribution for estimating a sound absorption coefficient in a reverberation room. The effectiveness of the proposed methods has been also experimentally confirmed by some applications to an actual stochastic evaluation of room lighting and acoustics.

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