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This paper proposes an auto-focusing (AF)-noise suppressor for cell-phone videos based on multiple noise references and spectral gain selection. AF noise generated by a lens actuator, a cam, or a helicoid is suppressed with a spectral gain which is calculated from a combination of multiple noise references prepared in advance. For quickly adjusting the spectral gain to product-dependent noise characteristics, a mixing ratio of noise references is adaptively determined based on an analysis result of the input noisy speech. The spectral gain for AF noise or that for background noise, whichever is smaller, is applied to the input noisy speech for actual suppression so that both AF noise and background noise are suppressed. Simulation results demonstrate that an AF noise recorded with a commercial cell-phone handset is successfully suppressed with little distortion in the target speech. The PESQ score is improved by as much as 2.9 compared to a conventional noise suppressor designed for ordinary background noise. This paper has been employed in commercial products which are shipped worldwide. [ABSTRACT FROM PUBLISHER]

Internet of things recently emerges as a common platform and service for consumer electronics. This paper presents an interactive framework of visualizing and authoring IoT in indoor environments such as home or small office. Building blocks of the framework are virtual sensors and actuators that abstract physical things and their virtual behaviors on top of their physical networks. Their behaviors are abstracted and programmed through visual authoring tools on the web, which allows a casual consumer to easily monitor and define their behaviors even without knowing the underlying physical connections. The user study performed to assess the usability of the visual authoring showed that the visual authoring is easy to use, understandable, and also preferred to typical text-based script programming. [ABSTRACT FROM AUTHOR]