EMD-based noise estimation and tracking (ENET) with application to speech enhancement

Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novel
approach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (EET) is presented with application to speech enhancement. Spectral analysis of nonstationary signals such as speech is performed effectively
using EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. EET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventional
log-MMSE filtering approach with better noise estimation, even during periods of strong speech activity.