Stanaćević Milutin, Li Shuo, Cauwenberghs Gert
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794-2350.
Department of Bioengineering, University of California San Diego, La Jolla, CA 92093.
IEEE Trans Circuits Syst I Regul Pap. 2016 Jul;63(7):972-981. doi: 10.1109/TCSI.2016.2556122. Epub 2016 Jun 29.
A parallel micro-power mixed-signal VLSI implementation of independent component analysis (ICA) with reconfigurable outer-product learning rules is presented. With the gradient sensing of the acoustic field over a miniature microphone array as a pre-processing method, the proposed ICA implementation can separate and localize up to 3 sources in mild reverberant environment. The ICA processor is implemented in 0.5 µm CMOS technology and occupies 3 mm × 3 mm area. At 16 kHz sampling rate, ASIC consumes 195 µW power from a 3 V supply. The outer-product implementation of natural gradient and Herault-Jutten ICA update rules demonstrates comparable performance to benchmark FastICA algorithm in ideal conditions and more robust performance in noisy and reverberant environment. Experiments demonstrate perceptually clear separation and precise localization over wide range of separation angles of two speech sources presented through speakers positioned at 1.5 m from the array on a conference room table. The presented ASIC leads to a extreme small form factor and low power consumption microsystem for source separation and localization required in applications like intelligent hearing aids and wireless distributed acoustic sensor arrays.
本文提出了一种采用可重构外积学习规则的并行微功耗混合信号VLSI独立分量分析（ICA）实现方案。通过将微型麦克风阵列上的声场梯度传感作为一种预处理方法，所提出的ICA实现方案能够在轻度混响环境中分离并定位多达3个声源。ICA处理器采用0.5 µm CMOS技术实现，占用面积为3 mm×3 mm。在16 kHz采样率下，该ASIC从3 V电源获取195 µW的功耗。自然梯度和赫劳尔特-朱顿ICA更新规则的外积实现方案在理想条件下表现出与基准FastICA算法相当的性能，并且在噪声和混响环境中具有更强的鲁棒性。实验表明，对于放置在会议室桌子上距离阵列1.5 m处的扬声器所呈现的两个语音源，在很宽的分离角度范围内都能实现清晰可感知的分离和精确的定位。所展示的ASIC为智能助听器和无线分布式声学传感器阵列等应用中所需的源分离和定位带来了超小尺寸和低功耗的微系统。
