MCRA noise estimation for KLT-VRE-based speech enhancement

A new signal subspace-based approach is proposed for the enhancement of speech corrupted by a high level of noise. Conventional subspace-based methods use the minimum mean square error criterion to optimize the Karhunen-Loève Transform (KLT). In non-stationary noisy environments, the selection of the optimal order of the KLT-based speech enhancement model is a critical issue. Indeed, estimation of the relevant subspace dimensions depends on the environmental conditions that may change unpredictably. Therefore, a drastic KLT-based dimension reduction may induce the loss of relevant components of speech and conversely, a reconstruction using a higher order of the KLT model will be ineffective to remove the noise. The method presented in this paper uses a Variance of Reconstruction Error (VRE) criterion to optimally select the KLT order model. A prominent point of this subspace method is that it incorporates the Minima Controlled Recursive Averaging (MCRA) to estimate the noise Power Spectral Density (PSD) used in the gain function. Three variants of the VRE combined with MCRA methods are implemented and compared, namely the VRE-MCRA, VRE-MCRA2 and VRE-IMCRA. Objective measures show that VRE-based approaches achieve a lower signal distortion and a higher noise reduction than existing enhancement methods.