Sparse Representations for Single Channel Speech Enhancement Based on Voiced/Unvoiced Classification

Abstract

The approach presented here in relies on a new voicing decision algorithm based on the multi-scale product (MP) characteristics. The MP is based on the multiplication of Wavelet Transform Coefficients at some scales. According to the voicing decision, improved subspace decomposition is operated on the voiced segments of the noisy speech signal and a multi-scale principal component analysis is applied on the unvoiced segments of the same signal. Furthermore, the voiced frames are decomposed into three subspaces: sparse, low rank, and the remainder noise components. Then, we calculate the components as a segregation problem. In the unvoiced frames, we combine the straightforward multivariate generalization of the wavelet denoising technique with the principal component analysis method. Experiments on NOIZEUS and NTT databases show that the proposed approach obtains satisfying results for most types of noise with little speech degradation and outperforms several competitive methods.