This report covers a robust voice activity detection (VAD) algorithm presented in [1]. The algorithm uses higher order statistics (HOS) metrics of speech signal in linear prediction coding (LPC) residual domain to classify noise and speech frames of a signal. Chapters in this report present voice activity detection problem and analysis of environment issues for VAD, deep HOS based and standard algorithms analysis and a real time HOS based voice activity detector model. New improvements (instantaneous SNR estimation, decision smoothing, adaptive thresholds, artificial neural network) to the proposed algorithm are introduced and performance results of the improved algorithm compared to standard VAD algorithms are presented.
| Identifer | oai:union.ndltd.org:LABT_ETD/oai:elaba.lt:LT-eLABa-0001:E.02~2006~D_20060529_131458-61965 |
| Date | 29 May 2006 |
| Creators | Duchovskis, Donatas |
| Contributors | Barauskas, Rimantas, Jasinevičius, Raimundas, Mockus, Jonas, Pranevičius, Henrikas, Plėštys, Rimantas, Maciulevičius, Stasys, Kanapeckas, Pranas, Kazanavičius, Egidijus, Telksnys, Laimutis, Kaunas University of Technology |
| Publisher | Lithuanian Academic Libraries Network (LABT), Kaunas University of Technology |
| Source Sets | Lithuanian ETD submission system |
| Language | Lithuanian |
| Detected Language | English |
| Type | Master thesis |
| Format | application/pdf |
| Source | http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060529_131458-61965 |
| Rights | Unrestricted |
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