Under a contamination of background sound noises, it seems difficult especially in a real working situation to evaluate various type statistics of only an objective sound signal fluctuation. In many cases of the noise evaluation, some signal processing method have been employed to eliminate the effect of background sound noises by first measuring emitted sound levels. In this study, a new evaluation method of sound level fluctuation is proposed in principle on the basis of the measurement of heterogeneous physical quantity other than sound pressures or sound levels to eliminate the effect of background sound noises. Though the theoretical analysis on acoustical emission caused by a mechanical vibration seems very difficult in a working situation, the sound noise fluctuation emitted only from an objective sound source can be effectively evaluated through its related vibration measurement by employing a fairly unified stochastic method proposed on the basis of a generalized regression analysis between sound and vibration. Here, the regression coefficients are determined by employing the least squares error method to minimize the mean square of estimation error to illustrate well the sound data by means of vibration data. Finally, the effectiveness of proposed method has been experimentally applied to the sound noise evaluation of a jigsaw.

A ner trial of statistical evaluation for a nonstationary traffic flow and its traffic noise is proposed as a prediction method of its probability distribution function by considering the temporal change of distribution parameters especially from a structural viewpoint. First, a headway distribution of the nonstationary traffic flow passing through within a road segment is proposed on the basis of an Erlang distribution by reflecting a temporal change of its distribution parameters. Then, an initial phase density concerning with asynchronous counting method and the probability of counting n cars over a long time interval are derived from the above nonstationary expression of headway distribution. Thus, the statistics of noise intensity at an observation point has been predicted by combining the above probabilistic factors and deterministic factors related to noise propagation environment with use of a compound stochastic process model. Finally, te effectivenss of the proposed theory has been confirmed experimentally by applying it to the actual traffic flow on a highway.