Abstract
In real sound environment system, a specific signal shows various types of probability distribution, and the observation data are usually contaminated by 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 input and output time series. Consequently, often the system input and output relationship in the real phenomenon cannot be represented by a simple model using only the linear correlation and lower order statistics. In this study, complex sound environment systems difficult to analyze by using usual structural method are considered. By introducing an estimation method of the system parameters reflecting correlation information for conditional probability distribution under existence of the external noise, a prediction method of output response probability for sound environment systems is theoretically proposed in a suitable form for the additive property of energy variable and the evaluation in decibel scale. The effectiveness of the proposed stochastic signal processing method is experimentally confirmed by applying it to the observed data in sound environment systems.
Highlights
A specific signal in real sound environment system usually exhibits multifarious and complex characteristics such as non-Gaussian distribution and nonlinear property relating to natural, social, or human factors
A stochastic signal processing method for predicting the output response probability distribution in decibel scale based on the input observations is proposed for complex sound environment systems
By paying attention to the energy variables satisfying the additive property of the specific signal and the external noise, a method for estimating the correlation information between the input and output variables has been theoretically derived on the basis of the observations contaminated by the external noise
Summary
A specific signal in real sound environment system usually exhibits multifarious and complex characteristics such as non-Gaussian distribution and nonlinear property relating to natural, social, or human factors. It was found that complex sound environment systems are difficult to analyze by using usual structural methods based on the physical mechanism [1]. The conditional probability density function contains the linear and nonlinear correlations in the expansion coefficients and these correlations play an important role as the statistical information for the input and output relationship of sound environment system. A stochastic signal processing method for predicting the output response probability distribution in decibel scale based on the input observations is proposed for complex sound environment systems. A method to estimate the system parameters reflecting several orders of correlation information between the input and output variables is derived by considering the additive property of energy variables under existence of external noise. The effectiveness of the proposed theory is confirmed experimentally by applying it to real data of a sound insulation system and the road traffic noise environment measured around a national road in Hiroshima city
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