ABSTRACT The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains uncertainty. Furthermore, the observations in the sound environment are often in the level-quantized form. In this paper, two types of methods for estimating the specific signal for sound envi-ronment systems with uncertainty and the quantized observation are proposed by introducing newly a system model of the conditional probability type and moment statistics of fuzzy events. The effectiveness of the proposed theoretical methods is confirmed by applying them to the actual problem of psychological evalua-tion for the sound environment.
Cite this paper
nullH. Orimoto and A. Ikuta, "Adaptive Method for State Estimation of Sound Environment System with Uncertainty and its Application to Psychological Evaluation," Intelligent Information Management, Vol. 2 No. 3, 2010, pp. 212-219. doi: 10.4236/iim.2012.23025.
 A. Ikuta, H. Masuike, and M. Ohta, “A digital filter for stochastic systems with unknown structure and its appli-cation to psychological evaluation of sound environ-ment,” IEICE Transactions on Information and Systems, Vol. E88-D, No. 7, pp. 1519–1522, 2005.
 S. Namba, S. Kuwano, and T. Nakamura, “Rating of road traffic noise using the method of continuous judgment by category,” The Journal of the Acoustical Society of Japan, Vol. 34, No. 1, pp. 29–34, 1978.
 A. Ikuta, M. Ohta, and M. N. H. Siddique, “Prediction of probability distribution for the psychological evaluation of noise in the environment based on fuzzy theory,” In-ternational Journal of Acousics and Vibration, Vol. 10, No. 3, pp. 107–114, 2005.
 R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of ASME, Series D, Journal of Basic Engineering, Vol. 82, No. 1, pp. 35–45, 1960.
 R. E. Kalman and R. S. Bucy, “New results in linear fil-tering and prediction theory,” Transactions of ASME, Se-ries D, Journal of Basic Engineering, Vol. 83, No. 1, pp. 95–108, 1961.
 H. J. Kushner, “Approximations to optimal nonlinear filter,” IEEE Transactions on Automatic Control, Vol. 12, No. 5, pp. 546–556, 1967.
 B. Bell and F. W. Cathey, “The iterated Kalman filter update as a Gauss-Newton methods,” IEEE Transactions on Automatic Control, Vol. 38, No. 2, pp. 294–297, 1993.
 K. Nishiyama, “A nonlinear filter for estimating a sinu-soidal signal and its parameter: On the case of a signal sinusoid,” IEEE Transactions on Signal Processing, Vol. 45, No. 5, pp. 970–981, 1997.
 T. L. Vincent and P. P. Khargonekar, “A class of nonlin-ear filtering problems arising from drift sensor gains,” IEEE Transactions on Automatic Control, Vo. 44, No. 3, pp. 509–520, 1999.
 S. Julier and J. Uhlmann, “Unscented filtering and nonlinear estimation,” Proceedings of The IEEE, Vol. 92, No. 3, pp. 401–421, 2004.
 G. Kitagawa, “Monte carlo filter and smoother for non-Gaussian nonlinear state space models,” Journal of Computational and Graphical Statistics, Vol. 5, No. 1, pp. 1–25, 1996.
 M. Ohta and H. Yamada, “New methodological trials of dynamical state estimation for the noise and vibration en-vironmental system,” Acustica, Vol. 55, No. 4, pp. 199– 212, 1984.
 A. Ikuta and M. Ohta, “A state estimation method of impulsive signal using digital filter under the existence of external noise and its application to room acoustics,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E75-A, No. 8, pp. 988–995, 1992.
 M. Ohta and A. Ikuta, “A basic theory of statistical gen-eralization and its experiment on the multi-variate state for environmental noise---A unification on the variate of probability function characteristics and digital or ana-logue type level observation,” The Journal of the Acous-tical Society of Japan, Vol. 39, No. 9, pp. 592–603, 1983.
 M. Ohta and T. Koizumi, “General statistical treatment of the response of a non-linear rectifying device to a sta-tionary random input,” IEEE Transactions on Information Theory, Vol. 14, No. 4, pp. 595–598, 1968.
 L. A. Zadeh, “Probability measures of fuzzy events,” Journal of Mathematical Analysis and Applications, Vol. 23, No. 2, pp. 421–427, 1968.