Time-frequency pattern wake-up detector for low-power always-on sensing of acoustic events

In acoustic event detection scenarios, average power consumed for continuous monitoring can be lowered by an low-power always-on sensor interface, waking-up power-hungry signal sampling and processing circuitry only upon detection of the specific audio-pattern. We research on power consumption of such wake-up sensing (WUS) interface consisting of a multichannel programmable analog filtering bank, signal detectors, and a digital wake-up pattern detector circuit. Focusing on the pattern detector, this paper investigates a design performing wake-up upon matching a sequence of its multi-channel inputs against a digitally preset template of time-frequency states. We compare two hardware implementations of a proposed design: one exploiting a dedicated programmable sequential logic chip, and the other on a low-power 16-bit microcontroller (MCU). In most cases, experimental results generally demonstrate lower energy expenditure obtained by the MCU. Only 330 nW is required for always-on listening, and 1.27–1.71 μJ is spent for analysis of a 3-channel sequence consisting of 4 successive states, each lasting for 200–500 ms. Depending on the application scenario, proposed WUS interface enables for extension of sensor node's battery-lifetime up to 7.4 times, compared to continuous sampling and processing.

[1]  Zhiyong Xu,et al.  Automatic detection, segmentation and classification of snore related signals from overnight audio recording , 2015, IET Signal Process..

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

[3]  Jörn Anemüller,et al.  Spectro-Temporal Gabor Filterbank Features for Acoustic Event Detection , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[4]  Michele Magno,et al.  Low-power multichannel spectro-temporal feature extraction circuit for audio pattern wake-up , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[5]  Jörn Anemüller,et al.  Automatic acoustic siren detection in traffic noise by part-based models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Stefan Goetze,et al.  Voice activity detection driven acoustic event classification for monitoring in smart homes , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[7]  Tadashi Shibata,et al.  Analog Soft-Pattern-Matching Classifier using Floating-Gate MOS Technology , 2001, NIPS.

[8]  Yun Li,et al.  Efficient Source Separation Algorithms for Acoustic Fall Detection Using a Microsoft Kinect , 2014, IEEE Transactions on Biomedical Engineering.

[9]  D. Grimaldi,et al.  Acoustic emission monitoring of damage concrete structures by multi-triggered acquisition system , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[10]  Vedran Bilas,et al.  Low-Power Wearable Respiratory Sound Sensing , 2014, Sensors.

[11]  Rainer Brück,et al.  AUDIS wear: A smartwatch based assistive device for ubiquitous awareness of environmental sounds , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  David Blaauw,et al.  A Dual-Stage, Ultra-Low-Power Acoustic Event Detection System , 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).

[13]  U. Antao,et al.  Low power, long life design for smart intelligence, surveillance, and reconnaissance (ISR) sensors , 2012, 2012 IEEE Conference on Technologies for Homeland Security (HST).

[14]  Reza Lotfian,et al.  A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks , 2011, 2011 International Conference on Body Sensor Networks.

[15]  A.G. Andreou,et al.  A wake-up detector for an acoustic surveillance sensor network: algorithm and VLSI implementation , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[16]  Vedran Bilas,et al.  Comparison of Power-Efficiency of Asthmatic Wheezing Wearable Sensor Architectures , 2016, S-CUBE.

[17]  R.P. Dick,et al.  Lucid Dreaming: Reliable Analog Event Detection for Energy-Constrained Applications , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[18]  Dan Stowell,et al.  On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[19]  Daniel P. W. Ellis,et al.  Spectral vs. spectro-temporal features for acoustic event detection , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).