Voice Activity Detector for Device with Small Processor and Memory

Sen, Tjong Wan

doi:10.1109/icsecc.2019.8907081

Cited by 3 publications

(9 citation statements)

References 11 publications

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“…A Neuromorphic hardware-friendly Spiking Neural Network (SNN) based VAD is presented in [11] but it is unclear whether actual neuromorphic hardware was used because the published power figures were based on estimates rather than actual measurements. A VAD design for low memory and computation power can be found in [16], but the implementation platform is not explicitly stated by the author. Usage of memory and processor hints either towards a digital or a mixed-signal platform.…”

Section: Literature Surveymentioning

confidence: 99%

“…An analog FEx with mixed-signal DT classifier is discussed in [19] where an ARM Cortex M4 ASIC Digital (or PDK sim.) [5], [7], [16], [18], [20], [21] ASIC Mixed-signal (or PDK sim.) [6], [9], [17], [19], [22]- [25] Total 21 papers a Digital FEx with classifier on Neuromorphic platform processor is used to generate the VAD output and re-train the DT classifier when the classification accuracy is poor.…”

Section: Literature Surveymentioning

confidence: 99%

“…A few papers mention the use of multiple sampling frequencies and sample widths. Multiple sampling frequencies (from 2kHz to 32kHz) are presented by [16] but which frequency is used to evaluate the VAD is not clearly stated. Multiple sample widths are presented by [24] where an approximate architecture is used to work on the reduced sample width when the SNR of the input signal is high.…”

Section: A Bandwidth and Dynamic Rangementioning

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%

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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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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: A Bandwidth and Dynamic Rangementioning

confidence: 99%

mentioning

confidence: 99%

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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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show abstract

“…This study also explains predictions according to supervised data about changes in the characteristics of disorders that have occurred [2]. The classification results using KNN are easy to understand and have good accuracy compared to other machine learning methods [3] or deep learning with newer approach using speech [4] or image [5] as input. The advantage of the KNN method is that it is effectively applied to large amounts of data and is resilient to noise training data, which is data that has the farthest range of values compared to other data but can disrupt the existing data structure.…”

Section: Introductionmentioning

confidence: 99%

Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM

Mutia

Sen²

2021

JISA

Self Cite

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Raw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the current raw material inventory. As a result, there are raw materials that are over order or lacking, which causes production to be constrained. The manual calculation method is not effective enough to meet the raw material inventory. In this research, the researcher proposes an algorithm which is contained in Data Mining, that is Enhanced KNN using GWO to predict raw material needs. Because GWO and Enhanced KNN algorithms give the results are easy to understand, have good accuracy compared to other machine learning methods, can cover the trapped problem from KNN traditional and capable of improving the accuracy using feature selection method. The method used in this study is to compare Enhanced KNN with and without GWO that gives a significant increase in the accuracy value by 16.5%, from 44.6% to 61.1%.

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Multivariate time series with Prophet Facebook and LSTM algorithm to predict the energy consumption

Riady¹,

Apriani²

2023

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)

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Voice Activity Detector for Device with Small Processor and Memory

Cited by 3 publications

References 11 publications

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

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

Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM

Multivariate time series with Prophet Facebook and LSTM algorithm to predict the energy consumption

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