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用于盲信号分离和自适应噪声消除的ICA算法的现场可编程门阵列实现

FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling.

作者信息

Kim Chang-Min, Park Hyung-Min, Kim Taesu, Choi Yoon-Kyung, Lee Soo-Young

机构信息

Dept. of Electr. Eng. and Comput. Sci., Korea Adv. Inst. of Sci. and Technol., Daejeon, South Korea.

出版信息

IEEE Trans Neural Netw. 2003;14(5):1038-46. doi: 10.1109/TNN.2003.818381.

Abstract

An field programmable gate array (FPGA) implementation of independent component analysis (ICA) algorithm is reported for blind signal separation (BSS) and adaptive noise canceling (ANC) in real time. In order to provide enormous computing power for ICA-based algorithms with multipath reverberation, a special digital processor is designed and implemented in FPGA. The chip design fully utilizes modular concept and several chips may be put together for complex applications with a large number of noise sources. Experimental results with a fabricated test board are reported for ANC only, BSS only, and simultaneous ANC/BSS, which demonstrates successful speech enhancement in real environments in real time.

摘要

本文报道了一种现场可编程门阵列(FPGA)实现的独立成分分析(ICA)算法,用于实时盲信号分离(BSS)和自适应噪声消除(ANC)。为了为基于ICA的多径混响算法提供强大的计算能力,在FPGA中设计并实现了一种特殊的数字处理器。该芯片设计充分利用了模块化概念,多个芯片可以组合在一起用于具有大量噪声源的复杂应用。文中报道了使用制造的测试板进行的仅ANC、仅BSS以及同时进行ANC/BSS的实验结果,这些结果表明该算法能够在实际环境中实时成功实现语音增强。

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