A 0.5 V 55 μW 64 x 2 Channel Binaural Silicon Cochlea for Event-Driven Stereo-Audio Sensing
A 0.5 V 55 μW 64 x 2 Channel Binaural Silicon Cochlea for Event-Driven Stereo-Audio Sensing
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DOI：
10.1109/jssc.2016.2604285
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发表时间：
2016-11-01
影响因子：
5.4
通讯作者：
Liu, Shih-Chii
中科院分区：
文献类型：
--
作者：
Yang, Minhao;Chien, Chen-Han;Liu, Shih-Chii
本文提出了一种64 × 2通道的立体声音频传感前端并行异步事件输出的启发生物耳蜗。每个双耳通道通过模拟带通滤波执行特征提取，并且经滤波的信号经由异步增量调制（ADM）被编码为事件。通道中心频率f（0）在人类听觉范围内几何缩放。两种设计技术被强调，以实现高的系统功率效率：基于源极跟随器的带通滤波器（BPF）和异步增量调制（ADM）与自适应自振荡比较。芯片采用0.18 μ m1 P6 M CMOS工艺制作，面积为10.5 × 4.8mm（2）。在0.5V电源下工作的核心耳蜗系统在100 k事件/s的输出速率下消耗55 μ W。f（0）的测量范围为8 Hz至20 kHz，BPF品质因数Q可从1调谐至几乎40。在Q近似为10时，在所有通道上，双耳之间的f（0）和Q的1 σ失配分别为3.3%和15%。从芯片的事件输出的语音输入的重建进行验证的信息完整性的事件域表示，和元音歧视被证明是一个简单的应用程序，使用输出事件的直方图。这种类型的硅耳蜗前端的目标是与嵌入式事件驱动处理器集成，以实现具有分类功能的低功耗智能音频感测，例如语音活动检测和扬声器识别。
This paper presents a 64 x 2 channel stereo-audio sensing front end with parallel asynchronous event output inspired by the biological cochlea. Each binaural channel performs feature extraction by analog bandpass filtering, and the filtered signal is encoded into events via asynchronous delta modulation (ADM). The channel central frequencies f(0) are geometrically scaled across the human hearing range. Two design techniques are highlighted to achieve the high system power efficiency: source-follower-based bandpass filters (BPFs) and asynchronous delta modulation (ADM) with adaptive self-oscillating comparison. The chip was fabricated in 0.18 mu m 1P6M CMOS, and occupies an area of 10.5x4.8 mm(2). The core cochlea system operating under a 0.5 V power supply consumes 55 mu W at an output rate of 100k event/s. The measured range of f(0) is from 8 Hz to 20 kHz, and the BPF quality factor Q can be tuned from 1 to almost 40. The 1 sigma mismatch of f(0) and Q between two ears is 3.3% and 15%, respectively, across all channels at Q approximate to 10. Reconstruction of speech input from the event output of the chip is performed to validate the information integrity in event-domain representation, and vowel discrimination is demonstrated as a simple application using histograms of the output events. This type of silicon cochlea front end targets integration with embedded event-driven processors for low-power smart audio sensing with classification capabilities, such as voice activity detection and speaker identification.