Keita Tanno

An Information Classification Approach Based on Knowledge Network

Numerous critical Internet applications with high-quality services, such as Web directory, search engine, Web crawler, recommendation system and user profile detector, etc. Almost depend on the efficient and accurate of web page classification system. Traditional supervised or semi-supervised machine learning methods become more and more difficult to adapt to the explosive Internet information. This paper proposed a web page classification method based on the topological structure of Wikipedia knowledge network. The kinship-relation association based on content similarity was proposed to solve the unbalance problem when a category node inherited the probability from multiple fathers. We used N-gram based on Wikipedia words to extract the keywords from web page, and introduce Bayes classifier to estimate the page class probability. Experimental results shown that the proposed method has very good scalability, robustness and reliability for different web pages.

Spatial direction estimation for multiple sound sources in reverberation environment

In this paper, we propose spatial localization of multiple sound sources using a spherical robot head equipped with four microphones. We obtain arrival time differences using phase difference candidates. Based on the model of precedence effect, arrival temporal disparities obtained from the zero-crossing point are used to calculate time differences and suppress the influence of echoes in a reverberant environment. To integrate spatial cues of different microphone pairs, we use a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound sources in azimuth-elevation localization, with the EA model even concurrently in reverberant environments.

Spatial localization of concurrent multiple sound sources

Based on the human auditory system for spatial localization theory, we proposed a spatial localization of multiple sound sources using a spherical robot head. Space sound vectors recorded by a microphone array with spatial configuration, are used to estimate the histograms of spatial arrival time difference vectors by solving the simultaneous equations in different frequency bands. The echo avoidance model based on precedence effect is used to reduce the interference of environment reverberations which provide the strong interference for phase vectors especially in small indoor environments. To integrate spatial cues of different microphone pairs, we propose a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound source in azimuth-elevation localization, even concurrently in reverberant environments.