Empirical mode decomposition for joint denoising and dereverberation

We propose a novel algorithm for the enhancement of noisy reverberant speech using empirical-mode-decomposition (EMD) based subband processing. The proposed algorithm is a one-microphone multistage algorithm. In the first step, noisy reverberant speech is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) via an EMD algorithm. Denoising is then applied to selected high frequency IMFs using EMD-based minimum means-quared error (MMSE) filter, followed by spectral subtraction of the resulting denoised high-frequency IMFs and low-frequency IMFs. Finally, the enhanced speech signal is reconstructed from the processed IMFs. The method was motivated by our observation that the noise and reverberations are disproportionally distributed across the IMF components. Therefore, different levels of suppression can be applied to the additive noise and reverberation in each IMF. This leads to an improved enhancement performance as shown in comparison to a related recent approach, based on the measurements by the signal-to-noise ratio (SNR).