A Dual-Stage, Ultra-Low-Power Acoustic Event Detection System

Chen, Yu; Cho, Minchang; Jeong, Seokhyeon; Blaauw, David; Sylvester, Dennis; Hun-Seok, Kim

doi:10.1109/sips.2016.45

Cited by 11 publications

(17 citation statements)

References 17 publications

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“…The comparison between the five approaches in term of system risk (energy-inefficiency), energy consumption, falsealarm and miss rates are given in Figures 11,12,13,14, respectively. From Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, µ is the adaptation step size. Then (13) is equivalent to (12), provided that the features' likelihood ratios are monotonic.…”

Section: B System Model and Optimizationmentioning

confidence: 99%

“…Cho et al [12] proposed a two-stage cascade architecture for an ultra low-power acoustic sensor. The first stage coarsely samples time-frequency (TF) characteristics of an audio stream and triggers the full TF analysis in the second stage if an acoustic event is detected.…”

Section: Related Workmentioning

confidence: 99%

“…, K that can be computed at train time. Intuitively, the thresholds η i in this implementation are updated to ensure that (13) produces decisions with the same probability measure q i as that of (12), consequently making them equivalent (assuming all features have monotonic likelihood ratios).…”

mentioning

confidence: 99%

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Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Jones

2017

IEEE Trans. Circuits Syst. I

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Abstract-Energy-efficiency is highly desirable for sensing systems in the Internet of Things (IoT). A common approach to achieve low-power systems is duty-cycling, where components in a system are turned off periodically to meet an energy budget. However, this work shows that such an approach is not necessarily optimal in energy-efficiency, and proposes guidedprocessing as a fundamentally better alternative. The proposed approach offers 1) explicit modeling of performance uncertainties in system internals, 2) a realistic resource consumption model, and 3) a key insight into the superiority of guided-processing over duty-cycling. Generalization from the cascade structure to the more general graph-based one is also presented. Once applied to optimize a large-scale audio sensing system with a practical detection application, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7× and 4× reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the dutycycling approach.

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Section: Resultsmentioning

confidence: 99%

“…Finally, µ is the adaptation step size. Then (13) is equivalent to (12), provided that the features' likelihood ratios are monotonic.…”

Section: B System Model and Optimizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Jones

2017

IEEE Trans. Circuits Syst. I

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“…Covering the last 12 years (Jan 2010 -Dec 2021), we found several hardware implementations of VAD (either mea- sured or simulated at the device level using foundry PDKs) [5]- [25]. This includes digital ( [5], [7], [16], [18], [20], [21]), analog ( [8], [10]), and mixed-signal ( [6], [9], [17], [19], [22]- [25]) designs along with some implementations on Field Programmable Gate Array (FPGA) ( [13], [14]), Field Programmable Analog Array (FPAA) ( [12]), Digital Signal Processor (DSP) ( [15]), and Neuromorphic platforms (e.g. Intel Loihi [26]) ( [11]), see Fig.…”

mentioning

confidence: 99%

Hardware Implementations for Voice Activity Detection: Trends, Challenges and Outlook

Yadav

Legaspi

Alink

et al. 2023

IEEE Trans. Circuits Syst. I

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Voice Activity Detection (VAD) is a technique used to identify the presence of human voice in an audio signal. It is implemented as an always-on component in most speech processing applications. As speech is absent most of the time, this component typically dominates the overall average power consumption of the system (excluding microphone). The widespread usage in speech applications and the need for ultra low power VAD have led to a plethora of algorithms and implementations in the hardware domain, necessitating a comprehensive study and analysis to understand (real-time) requirements, different design parameters, testing strategies, but also to identify design trends, challenges and guidelines for future implementations and testing of VAD devices. A scoping review was conducted to identify the articles for hardware implementations of VAD from January 2010 -December 2021, the results of which are presented in this article. The results highlight a big design space being used for VAD along with a lack of standard testing methodology and usage of application-dependent performance metrics. An increased usage of filter-based feature extractors along with neural-networkbased classifiers is observed. Due to lack of standardisation, no other trends can be established from the results. A set of rules and guidelines are therefore provided to facilitate the future development and benchmarking of VADs.

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