Most of actual environmental systems show a complicated fluctuation pattern of non-Gaussian type, owing to various kinds of factors. In the actual measurement, the fluctuation of random signal is usually contaminated by an external noise. Furthermore, it is very often that the reliable observation value can be obtained only within a definite fluctuating amplitude domain, because many of measuring equipments have their proper dynamic range and original random wave form is unreliable at the end of amplitude fluctuation. It becomes very important to establish a new signal detection method applicable to such an actual situation. This paper newly describes a dynamical state estimation algorithm for a successive observation contaminated by the external noise of an arbitrary distribution type, when the observation value is measured through a finite dynamic range of measurement. On the basis of the Bayes' theorem, this method is derived in the form of a wide sense digital filter, which is applicable to the non-Gaussian properties of the fluctuations, the actual observation in a finite amplitude domain and the existence of external noise. Differing from the well-known Kalman's filter and its improvement, the proposed state estimation method is newly derived especially by paying our attention to the statistical information on the observation value behind the saturation function instead of that on the resultant noisy observation. Finally, the proposed method is experimentally confirmed too by applying it to the actual problem for a reverberation time measurement from saturated noisy observations in room acoustics.

This paper proposes a method to estimate the waveform of a specified sound source in a noisy and reverberant environment using a sensor array. Previously, we proposed an iterative method to estimate the waveform. However, in this method the effect of reflection sound reduces to 1/M, where M is the number of microphones. Therefore, to solve the reverberation problem, we propose a new method using inverse filters of the transfer functions from the sound sources to each microphone. First, the transfer function from each sound source to each microphone is measured by the cross-spectrum technique and each inverse filter is calculated by the QR method. Then the initially estimated waveform of a sound source is the averaged signal of the inverse filter outputs. Since this waveform still contains the effects of the other sound sources, the iterative technique is adopted to estimate the waveform more precisely, reducing the effects of the other sound and the reflection sound. Some computer simulations and experiments were carried out. The results show the effectiveness of our method.

The development of computers capable of handling complex objects requires nonverbal interfaces that can bidirectionally mediate nonverbal communication including the gestures of both people and computers. Nonverbal expressions are poweful media for enriching and facilitating humancomputer interaction when used as interface languages. Four gestural modes are appropriate for human-computer interaction: the sign, indication, illustration and manipulation modes. All these modes can be conveyed by a generalized gesture interface that has specific processors for each mode. The basic component of the generalized gesture interface, a gesture dictionary, is proposed. The dictionary can accept sign and indicating gestures in which postures or body shapes are significant, pass their meaning to a computer and display gestures from the computer. For this purpose it converts body shapes into gestural codes by means of two code systems and, moreover, it performs bidirectional conversions of several gesture representations. This dictionary is applied to the translation of Japanese into sign language; it displays an actor who speaks the given Japanese sentences by gesture of sign words and finger alphabets. The performance of this application confirms the adequacy and usefulness of the gesture dictionary.