Speech-stream detection with low signal-to-noise ratios based on empirical mode decomposition and fourth-order statistics

Speech-stream detection plays an important role in short-wave communication. It is tiring for a person to listen something for a long time, especially in adverse environments. An algorithm for speech-stream detection in noisy environments, based on the empirical mode decomposition (EMD) and the statistical properties of higher-order cumulants of speech signals is presented. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant (FOC) can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order cumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted, by Gaussian noise. Besides that, with the self-adaptive decomposition by the EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNR, and the algorithm is robust in the real signal tests.