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Voice activity detection in the DFT domain based on a parametric noise model
- Author(s)
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- Colin Breithaupt, (Ruhr-Universität Bochum)
- Rainer Martin, (Ruhr-Universität Bochum)
- Topics
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- Voice activity detection and double-talk detection
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Abstract
We present a robust voice activity detection (VAD) algorithm which is based on the statistics of the coefficients of the discrete Fourier transform (DFT) derived from short signal segments. This algorithm uses a common parametric noise probability density function (PDF) in all frequency bins. The noise model is based on a Rayleigh inverse Gaussian distribution that is adapted to the statistics of the noise
during speech-absence. As only the current and past signal frames are analysed, the detection is causal and no additional delay is introduced. A framework for protecting low energy syllables at the end of utterances is also described.
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