A voice activity detection using cyclic statistics based on sinusoidal speech model

In this paper, by using the cyclostationary properties of speech signal, a voice activity detection (VAD) algorithm based on cyclic cumulant is proposed. The proposed scheme employs the third-order cyclic cumulant of the LPC residual of a speech signal. Analytical expressions for the third-order cyclic cumulant of the LPC residual of short-term speech are derived assuming a sinusoidal model. Matrix pencil method (MP) are adopted to estimate the frequencies of harmonic signal contained in LPC residual of speech signal, which are used as the cyclic frequencies of cyclic cumulant. Then the third-order cyclic cumulant is defined and used to construct the VAD detection variation. The test results show that the proposed algorithm gives better results than G.729B VAD.