This paper shows the effectiveness of a cinematographic camera for controlling 3D video by measuring its effects on viewers with several typical camera works. 3D free-viewpoint video allows us to set its virtual camera on arbitrary positions and postures in 3D space. However, there have been neither investigations on adaptability nor on dependencies between the camera parameters of the virtual camera (i.e., positions, postures, and transitions) nor the impressions of viewers. Although camera works on 3D video based on expertise seems important for making intuitively understandable video, it has not yet been considered. When applying camera works to 3D video using the planning techniques proposed in previous research, generating ideal output video is difficult because it may include defects due to image resolution limitation, calculation errors, or occlusions as well as others caused by positioning errors of the virtual camera in the planning process. Therefore, we conducted an experiment with 29 subjects with camera-worked 3D videos created using simple annotation and planning techniques to determine the virtual camera parameters. The first point of the experiment examines the effects of defects on viewer impressions. To measure such impressions, we conducted a semantic differential (SD) test. Comparisons between ground truth and 3D videos with planned camera works show that the present defects of camera work do not significantly affect viewers. The experiment's second point examines whether the cameras controlled by planning and annotations affected the subjects with intentional direction. For this purpose, we conducted a factor analysis for the SD test answers whose results indicate that the proposed virtual camera control, which exploits annotation and planning techniques, allows us to realize camera working direction on 3D video.

We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.