A low power Voice Activity Detector for portable applications

Meoni, Gabriele; Pilato, Luca; Fanucci, Luca

doi:10.1109/prime.2018.8430328

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Power consumption Bin encoding 3.8µW (lower bound) Yang et al [5] 1µW Price et al [3] 22µW Meoni et al [4] 559µW Table 1. Energy consumption of bin encoding and baselines.…”

Section: Methodsmentioning

confidence: 99%

“…Power consumption Bin encoding 3.8µW (lower bound) Yang et al [5] 1µW Price et al [3] 22µW Meoni et al [4] 559µW…”

Section: Methodsmentioning

confidence: 99%

“…A VAD system belongs to the basic building blocs of any audio and speech processing system, and, as it is always on, it needs to be power efficient. The VAD systems are well studied and recently several very low power algorithms [3,4] and the circuits [5] were proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

Dellaferrera

Martinelli

Cerňak

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Advances of deep learning for Artificial Neural Networks (ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital chips. Although recent progress in low-power chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs) based applications offer an even lower power consumption, as a consequence of the ensuing sparse spikebased coding scheme. In this work, we develop a SNN-based Voice Activity Detection (VAD) system that belongs to the building blocks of any audio and speech processing system. We propose to use the bin encoding, a novel method to convert log mel filterbank bins of single-time frames into spike patterns. We integrate the proposed scheme in a bilayer spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our approach shows that SNNs enable an ultra lowpower implementation of a VAD classifier that consumes only 3.8µW, while achieving state-of-the-art performance.

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“…Power consumption Bin encoding 3.8µW (lower bound) Yang et al [5] 1µW Price et al [3] 22µW Meoni et al [4] 559µW Table 1. Energy consumption of bin encoding and baselines.…”

Section: Methodsmentioning

confidence: 99%

“…Power consumption Bin encoding 3.8µW (lower bound) Yang et al [5] 1µW Price et al [3] 22µW Meoni et al [4] 559µW…”

Section: Methodsmentioning

confidence: 99%

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A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

Dellaferrera

Martinelli

Cerňak

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…a [13], [14] does not give any quantitative performance metric b [9] gives only plot for SHR vs. NSHR recording and processing information along with the details about the speakers.…”

Section: E Speech and Noise Datasetsmentioning

confidence: 99%

“…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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Fully Digital Low-Power Implementation of an Audio Front-End for Portable Applications

Meoni

Pilato

Ciarpi

et al. 2019

Lecture Notes in Electrical Engineering

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A low power Voice Activity Detector for portable applications

Cited by 10 publications

References 5 publications

A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

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

Fully Digital Low-Power Implementation of an Audio Front-End for Portable Applications

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