Static and dynamic state estimation methods for sound environment based on fuzzy observation

In the actual sound environment, a specific signal shows various types of probability distribution forms apart from a standard Gaussian distribution due to the diversified causes of fluctuation. Furthermore, the actually observed data often contain some fuzziness due to the existence of confidence interval in measuring instruments, permissible error in experimental data and a level quantized error in digital observation. In this study, a new estimation theory for a specific signal, based on the observed data containing the fuzziness and the effects of nonGaussian property is proposed from the static and dynamic viewpoints. The validity and the effectiveness of the proposed method are confirmed experimentally by applying it to the actually observed data in sound environment