In the actual sound environment system, a specific signal shows various types of probability distribution, and the observation data are contaminated by an external noise (e.g., background noise) of non-Gaussian distribution type. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between the system input and output time series. Consequently, the relationship between the system input and output cannot be represented by a simple linear model based on only the linear correlation and lower order statistics. In this paper, a complex sound environment system difficult to analyze by using usual structural method is considered. By introducing a nonlinear system model based on conditional probability distribution with various correlations between input and output signals, a prediction method of output response probability for sound environment systems is theoretically proposed in a suitable form for the system with nonlinear, non-Gaussian and nonstationary properties. The effectiveness of the proposed method is experimentally confirmed by applying it to the observed data in sound environment.
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