No abstract

No abstract

No abstract

SUMMARYIn this paper, we propose frequency assignment as a method for multichannel speech separation. In this method, the magnitudes of the absolute values of each component in the frequency domain are compared, then based on this comparison result determines to which channel a frequency component originally belongs. The speech is separated by assigning the frequency to the determined channel. Therefore, it becomes possible to separate multichannel speech with a low computational load. However, it has the restriction that each speaker must speak close to his microphone. For situations in which this premise does not hold, we present a solution that uses beam-forming toward the sound sources. We also show the application of the frequency assignment to sound source localization. In this paper, we demonstrate the usefulness of the proposed method through simulation tests and tests in a realistic environment.

SUMMARYIn this paper, a novel method of surface reconstruction from a single monocular image is proposed. Our proposed approach (called "shape from object-specific knowledge") is based on knowledge of objects, acquired by learning from a number of samples. To achieve this, we investigate the use of the Subband Pseudo 2D Hidden Markov Model (SPHMM), which is an extended version of normal Pseudo 2D HMMs. SPHMMs can model the correspondence between an intensity image and its depth information. We have applied our algorithm to 3D face, 3D hand, and 3D car reconstruction from single images, and the results show the effectiveness of the proposed method.