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2 . INPUT-OUTPUT RELATIONSHIP IN A COMPLICATED SOUND ENVIRONMENT

An actual sound environment often shows complicated fluctuation patterns apart from a usual Gaussian type. Various evaluation procedures for the sound environment were methodologically proposed, owing to the variety of phenomena and the complexity of human response to them. It is very difficult to evaluate precisely the proper characteristics of complicated systems only from a physical viewpoint based on the structural mechanism. In such situation, a regression analysis function is usually employed between the input and the output fluctuations especially under the assumption of Gaussian property for the input-output fluctuations and/or the usual least squares error criterion. Furthermore, the extended regression analysis method which we previously reported was necessarily reduced in a complicated mathematical form, though it could utilize the lower and higher order correlations on the basis of Bayes’ theorem. This paper describes a regression analysis matched to the prediction of the output response probability for complicated sound environmental systems by introducing a hierarchical neural network with statistical structure. Then, based on the estimated result, the output probability can be easily predicted for the same system with arbitrary input signal. Finally, the effectiveness of the present method is experimentally confirmed by applying it to the actually observed data.