Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics

QIAN Zhen, LI Xue-yao, ZHANG Ru-bo, WangWu

College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China



Abstract:

To capture the presence of speech embedded in nonspeech events and background noise in short-wave non-cooperative communication,an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition (EMD) and statistical properties of higher-order cumulants of speech signals.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.With the self-adaptive decomposition by 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 SNRs,and the algorithm is robust in real signal tests.

Key words:  speech-stream detection  higher-order statistics  Empirical Mode Decomposition

DOI：10.11916/j.issn.1005-9113.2009.05.024

Clc Number:TN912.3

Fund: