Voice Activity Detection Based on Complex Exponential Atomic Decomposition and Likelihood Ratio Test

The voice activity detection (VAD) algorithms by using Discrete Fourier Transform (DFT) coefficients are widely found in literature. However, some shortcomings for modeling a signal in the DFT can easily degrade the performance of a VAD in noise environment. To overcome the problem, this paper presents a novel approach by using the complex coefficients derived from complex exponential atomic decomposition of a signal. Those coefficients are modeled by a complex Gaussian probability distribution and a statistical model is employed to derive the decision rule from the likelihood ratio test. According to the experimental results, the proposed VAD method shows better performance than the VAD based on DFT coefficients in various noise environments.