The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. Furthermore, the observations in the sound environment are often in the level-quantized form. In this paper, a method for estimating the specific signal for stochastic systems with unknown structure and the quantized observation is proposed by introducing a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem of psychological evaluation for the sound environment.

In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.

The observed phenomena in actual sound and electromagnetic environment are inevitably contaminated by the background noise of arbitrary distribution type. Therefore, in order to evaluate sound and electromagnetic environment, it is necessary to establish some signal processing methods to remove the undesirable effects of the background noise. In this paper, we propose noise cancellation methods for estimating a specific signal with the existence of background noise of non-Gaussian distribution from two viewpoins of static and dynamic signal processing. By applying the well-known least mean squared method for the moment statistics with several orders, practical methods for estimating the specific signal are derived. The effectiveness of the proposed theoretical methods is experimentally confirmed by applying them to estimation problems in actual sound and magnetic field environment.

In the measurement of actual random phenomenon, the observed data often result in a loss or are distorted due to the existence of a definite dynamic range of measurement instruments. In this study, a trial on the stochastic signal processing for the incomplete data with loss or distortion is newly proposed. Concretely, by regarding the observed data within a finite dynamic measurement range as a random variable with a amplitude saturation, a new unified expression of the probability distribution function matched to this amplitude limitation is derived in a series expansion form. Next, as an application of the above probability expression, a state estimation method based on the above incomplete observations is theoretically proposed through an establishment of wide sence digital filter under the actual situation of existence of the additional noise. Finally, the validity and the effectiveness of the proposed method are experimentally confirmed.

In this paper, a stochastic signal processing method of analog type for the prediction of power state variable is first discussed by considering the effect of an internal mechanism of an instrument with mean squaring operation. Next, in the actual case of quantized observation contaminated by an additive random noise, a wide sense digital filter estimating the power state variable of stochastic systems is proposed. These methods are applied to the actual data measured in acoustic environment.

In this study, an expression of the regression relationship with less information loss is concretely derived in the form suitable to the existence of amplitude constraint of the observed data and the prediction of response probability distribution. The effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.

It often occurs in the acoustic environment that a specific signal is contaminated by the additional noise of non-Gaussian distribution type. In order to extract exactly the various statistical information of only specific signal from the observed noisy data, a stochastic signal processing by use of digital computer is essential. In this study, a stochastic method for estimating the probability function of the specific signal embedded in the additional noise is first theoretically proposed in a suitable form for the quantized level observation. Then, the effectiveness of the proposed method is experimentally confirmed by applying it to the observed data in the acoustic environment.

It often occurs in an environmental phenomenon in our daily life that a specific signal is partially or completely contaminated by the additional external noise. In this study, a digital filter for estimating a specific signal fluctuating impulsively under the existence of an actual external noise with various kinds of probability distribution forms is proposed in an improved form of already reported digital filter. The effectivenss of the proposed theory is experimentally confirmed by applying it to the estimation of an actual impulsve signal in a room acoustic.

In the measurement of actual random phenomenon, the observed data often contain the fuzziness due to the existence of confidence limitation in measuring instruments, permissible error in experimental data, some practical simplification of evaluation procedure and a quantized error in digitized observation. In this study, by introducing the well-known fuzzy theory, a state estimation method based on the above fuzzy observations is theoretically proposed through an establishment of wide sense digital filter under the actual situation of existence of the background noise in close connection of the inverse problem. The validity and effectiveness of the proposed method are experimentally confirmed by applying it to the actual fuzzy data observed in an acoustic environment.

In this paper, a new restoration method for the X-ray images with optical blurs and quantum mottles is proposed by considering the physical formation process of X-ray images. More specifically, the optical blurs are first deterministically cleared away by using the transfer characteristic of the laser scanning, the characteristic of the radiographic screen-film system, the logarithmic transformation of the optical density and a digital inverse filter based on the point spread function. Next, as a restoration method for remaining quantum mottles, a wide sense digital filter of a stochastic type using the statistical properties of quantum mottles is newly derived on the basis of a Bayes' theorem matched to the recursive image processing. Finally, in order to confirm the effectiveness of the proposed method, it is applied to one of the actual medical images.

The processes observed in a sound environment inevitably contain additional external noise of arbitrary distribution. Furthermore, the actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. In this paper, a method for estimating the input signal for a sound environment system with unknown structure and additive noise of arbitrary probability distribution is proposed by introducing a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed experimentally by applying it to the actual problem of input estimation of the sound environment.

In this study, after focussing on an energy (or intensity) scaled variable of acoustic systems, first, a new regression analysis method is theoretically proposed by introducing a multiplicative noise model suitable to the positively scaled stocastic system. Then, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.