A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640512 pixel input images can be processed in real-time with three agents at a rate of 9 fps in 48 MHz operation.

A sound localization method in the proximal region is proposed, which is based on a low-cost 3D sound localization algorithm with the use of head-related transfer functions (HRTFs). The auditory parallax model is applied to the current algorithm so that more accurate HRTFs can be used for sound localization in the proximal region. In addition, head-shadowing effects based on rigid-sphere model are reproduced in the proximal region by means of a second-order IIR filter. A subjective listening test demonstrates the effectiveness of the proposed method. Embedded system implementation of the proposed method is also described claiming that the proposed method improves sound effects in the proximal region only with 5.1% increase of memory capacity and 8.3% of computational costs.

In this paper, we propose a new 3D sound rendering method for multiple sound sources with limited computational resources. The method is based on fuzzy clustering, which achieves dual benefits of two general methods based on amplitude-panning and hard clustering. In embedded systems where the number of reproducible sound sources is restricted, the general methods suffer from localization errors and/or serious quality degradation, whereas the proposed method settles the problems by executing clustering-process and amplitude-panning simultaneously. Computational cost evaluation based on DSP implementation and subjective listening test have been performed to demonstrate the applicability for embedded systems and the effectiveness of the proposed method.