The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La parole peut être modélisée sous forme de courtes bouffées d’énergie vocale séparées par des espaces de silence. Au cours d'une conversation typique, les jets de discussion ne représentent que 40 % du discours de chaque partie et les 60 % restants sont constitués de silence. Les systèmes de communication peuvent obtenir un gain spectral en déconnectant les utilisateurs de la ressource spectrale pendant les périodes de silence. Cette lettre développe un algorithme de détection d'activité vocale (VAD) simple et efficace pour fonctionner dans un environnement mobile présentant un bruit de fond variant dynamiquement. Le VAD utilise une méthode de classification impliquant l'énergie de la bande complète, le rapport entre l'énergie de la bande basse et l'énergie de la bande complète, le taux de passage par zéro et la mesure de la crête.
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Jae Won KIM, Min Sik SEO, Byung Sik YOON, Song In CHOI, Young Gap YOU, "A Voice Activity Detection Algorithm for Wireless Communication Systems with Dynamically Varying Background Noise" in IEICE TRANSACTIONS on Communications,
vol. E83-B, no. 2, pp. 414-418, February 2000, doi: .
Abstract: Speech can be modeled as short bursts of vocal energy separated by silence gaps. During typical conversation, talkspurts comprise only 40% of each party's speech and remaining 60% is silence. Communication systems can achieve spectral gain by disconnecting the users from the spectral resource during silence periods. This letter develops a simple and efficient Voice Activity Detection (VAD) algorithm to work in a mobile environment exhibiting dynamically varying background noise. The VAD uses a classification method involving the full-band energy, ratio of low-band energy to full-band energy, zero-crossing rate, and peakiness measure.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e83-b_2_414/_p
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@ARTICLE{e83-b_2_414,
author={Jae Won KIM, Min Sik SEO, Byung Sik YOON, Song In CHOI, Young Gap YOU, },
journal={IEICE TRANSACTIONS on Communications},
title={A Voice Activity Detection Algorithm for Wireless Communication Systems with Dynamically Varying Background Noise},
year={2000},
volume={E83-B},
number={2},
pages={414-418},
abstract={Speech can be modeled as short bursts of vocal energy separated by silence gaps. During typical conversation, talkspurts comprise only 40% of each party's speech and remaining 60% is silence. Communication systems can achieve spectral gain by disconnecting the users from the spectral resource during silence periods. This letter develops a simple and efficient Voice Activity Detection (VAD) algorithm to work in a mobile environment exhibiting dynamically varying background noise. The VAD uses a classification method involving the full-band energy, ratio of low-band energy to full-band energy, zero-crossing rate, and peakiness measure.},
keywords={},
doi={},
ISSN={},
month={February},}
Copier
TY - JOUR
TI - A Voice Activity Detection Algorithm for Wireless Communication Systems with Dynamically Varying Background Noise
T2 - IEICE TRANSACTIONS on Communications
SP - 414
EP - 418
AU - Jae Won KIM
AU - Min Sik SEO
AU - Byung Sik YOON
AU - Song In CHOI
AU - Young Gap YOU
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E83-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2000
AB - Speech can be modeled as short bursts of vocal energy separated by silence gaps. During typical conversation, talkspurts comprise only 40% of each party's speech and remaining 60% is silence. Communication systems can achieve spectral gain by disconnecting the users from the spectral resource during silence periods. This letter develops a simple and efficient Voice Activity Detection (VAD) algorithm to work in a mobile environment exhibiting dynamically varying background noise. The VAD uses a classification method involving the full-band energy, ratio of low-band energy to full-band energy, zero-crossing rate, and peakiness measure.
ER -