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Yoko YAMAKATA Michiaki KATSUMOTO Toshiyuki KIMURA
In this paper, we propose a new system for controlling radiated sound directivity. The proposed system artificially induces a bending vibration on a planar diaphragm by vibrating it artificially using multiple vibrators. Because the bending vibration in this case is determined by not one but all of the accelerated vibrations, the vibration of the diaphragm can be controlled by modulating the accelerated vibration waveforms relatively for each frequency. As a consequence, the directivity of the radiated sound is also varied. To investigate the feasibility of this system, we constructed a prototype that has for a diaphragm a circular plate-one of the most typical shapes considered for discussing plate vibration-and three vibrators. The measurement data showed visually that with this system, surface vibration and sound directivity change depending on the phases of the accelerated vibrations.
Masashi ANZAWA Sosuke AMANO Yoko YAMAKATA Keiko MOTONAGA Akiko KAMEI Kiyoharu AIZAWA
We investigate image recognition of multiple food items in a single photo, focusing on a buffet restaurant application, where menu changes at every meal, and only a few images per class are available. After detecting food areas, we perform hierarchical recognition. We evaluate our results, comparing to two baseline methods.
Toshiyuki KIMURA Yoko YAMAKATA Michiaki KATSUMOTO Kazuhiko KAKEHI
Although it is very important to conduct listening tests when constructing a practical sound field reproduction system based on wave field synthesis, listening tests are very expensive. A localization model of synthesized sound images that predicts the results of listening tests is proposed. This model reduces the costs of constructing a reproduction system because it makes it possible to omit the listening tests. The proposed model uses the precedence effect and predicts the direction of synthesized sound images based on the inter-aural time difference. A comparison of the results predicted by the proposed model and the localized results of listening tests shows that the model accurately predicts the localized results.