naa a22 4500 510797970 CHVBK 20180411083343.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11277-013-1015-1 doi (NATIONALLICENCE)springer-10.1007/s11277-013-1015-1 VAD Based on Kernel Smoothed Function of EGARCH Models [Elektronische Daten] [Usoph Salemi, Sadegh Rezaei, Saralees Nadarajah] An algorithm for a voice activity detector (VAD) is proposed. It is based on the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) filter for generalized hyperbolic (GH), Gaussian random variables, adaptive threshold values and autocorrelation coefficients. EGARCH models are a new variation of GARCH models used especially in economic time series. A speech signal is assumed to have a GH because GH has heavier tails than the Gaussian distribution (GD) covering other heavy tailed distributions like hyperbolic, skewed $$t$$ , variance gamma (VG), inverse Gaussian (NIG), Cauchy, Student's $$t$$ and Laplace distributions. The distribution of noise signal is assumed to be uncorrelated (white noise), but in general, that is not necessary. In the proposed method, heteroscedasticity is modeled by EGARCH. A kernel smoothed function of conditional variances and autocorrelations generate the soft detection vector. Finally, hard detection is the result of comparing the soft detection vector with an adaptive threshold value. The simulation results show that the proposed VAD is able to operate down to $$-5$$ dB. Springer Science+Business Media New York, 2013 EGARCH models nationallicence GARCH models nationallicence Generalized hyperbolic distribution nationallicence Voice activity detection nationallicence Salemi Usoph Department of Statistics, Amirkabir University of Technology, Tehran, Iran aut Rezaei Sadegh Department of Statistics, Amirkabir University of Technology, Tehran, Iran aut Nadarajah Saralees School of Mathematics, University of Manchester, M13 9PL, Manchester, UK aut Wireless Personal Communications Springer US; http://www.springer-ny.com 72/1(2013-09-01), 299-313 0929-6212 72:1<299 2013 72 11277 https://doi.org/10.1007/s11277-013-1015-1 text/html Onlinezugriff via DOI 1 brief-communication jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11277-013-1015-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Salemi Usoph Department of Statistics, Amirkabir University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Rezaei Sadegh Department of Statistics, Amirkabir University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Nadarajah Saralees School of Mathematics, University of Manchester, M13 9PL, Manchester, UK aut NATIONALLICENCE 773 0- Wireless Personal Communications Springer US; http://www.springer-ny.com 72/1(2013-09-01), 299-313 0929-6212 72:1<299 2013 72 11277 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer