An energy-efficient spike encoding circuit for speech edge detection

In speech processing applications, the instantaneous bandwidth of speech can be used to adaptively control the performance of an audio sensor’s analog front end. Extracting the instantaneous bandwidth of speech depends on the detection of speech edges in the time–frequency plane. In this paper, we propose a spike encoding circuit for real-time and low-power speech edge detection. The circuit can directly encode the signal’s envelope information—an important feature to identify the speech edge—by temporal spike density without additional envelope extraction. Furthermore, the spike encoding circuit automatically adapts its resolution to the amplitude of the input signal, which improves the encoding resolution for small signal without increasing the power consumption. We use the nonlinear dynamical approach to design this circuit and analyze its stability. We also develop a linearized model for this circuit to provide the design intuition and to explain its adaptive resolution. Fabricated in 0.5-μm CMOS process, the spike encoding circuit consumes 0.3-μW power and the experimental results are presented.

[1]  Alister Hamilton,et al.  An asynchronous spike event coding scheme for programmable analog arrays , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[2]  Hiroyuki Mino,et al.  Encoding of Information Into Neural Spike Trains in an Auditory Nerve Fiber Model With Electric Stimuli in the Presence of a Pseudospontaneous Activity , 2007, IEEE Transactions on Biomedical Engineering.

[3]  DeLiang Wang,et al.  Auditory Segmentation Based on Onset and Offset Analysis , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  Oded Ghitza,et al.  Auditory nerve representation as a front-end for speech recognition in a noisy environment , 1986 .

[5]  Yannis P. Tsividis,et al.  Event-Driven Data Acquisition and Digital Signal Processing—A Tutorial , 2010, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  Dingkun Du,et al.  An adaptive microphone preamplifier for low power applications , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[7]  Yannis P. Tsividis,et al.  Event-driven, continuous-time ADCs and DSPs for adapting power dissipation to signal activity , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[8]  Dingkun Du,et al.  Efficient speech edge detection for mobile health applications , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[9]  David J. Goodman,et al.  Delta modulation granular quantizing noise , 1969 .

[10]  Michael Christoph Büchler,et al.  Algorithms for sound classification in hearing instruments , 2002 .

[11]  Martin H. Weik Computer Science and Communications Dictionary , 2000 .

[12]  Giacomo Indiveri,et al.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity , 2006, IEEE Transactions on Neural Networks.

[13]  R. E. Kalman,et al.  Control System Analysis and Design Via the “Second Method” of Lyapunov: I—Continuous-Time Systems , 1960 .

[14]  Leslie S. Smith,et al.  Robust sound onset detection using leaky integrate-and-fire neurons with depressing synapses , 2004, IEEE Transactions on Neural Networks.

[15]  Dingkun Du,et al.  A bio-inspired ultra-low-power spike encoding circuit for speech edge detection , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[16]  Yannis P. Tsividis Event-driven data acquisition and continuous-time digital signal processing , 2010, IEEE Custom Integrated Circuits Conference 2010.

[17]  Hynek Hermansky,et al.  Fully integrated 500uW speech detection wake-up circuit , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[18]  Robert E. Sandlin Textbook of hearing aid amplification , 2000 .

[19]  Michael T. Johnson,et al.  Auditory coding based speech enhancement , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[20]  John G. Harris,et al.  Spike-Based Feature Extraction for Noise Robust Speech Recognition Using Phase Synchrony Coding , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[21]  Michael S. Lewicki,et al.  Efficient auditory coding , 2006, Nature.