Single-Channel Speech Enhancement Based on Adaptive Low-Rank Matrix Decomposition

The low-rank matrix decomposition (LMD) algorithm based on the maximum correntropy criterion (MCC) has recently shown its superiority to other algorithms in classification (e.g., face recognition), and we develop it into single-channel speech enhancement for the low-rank structure of speech signals in the time domain. However, a new issue has arisen: some residual noise exists in the enhanced speech due to its sensitivity to the exact rank value. To address this issue, we propose a novel adaptive LMD (ALMD) algorithm in which the energy threshold technique is adopted to adaptively update the effective rank value of each frame of the speech matrix. Our proposed ALMD algorithm can achieve an acceptable performance for low signal-to-noise ratio (SNR) levels without approximating the speech phase with the noisy phase. We compare ALMD algorithm with common conventional algorithms in Gaussian white noise and non-Gaussian noise conditions. The simulation results demonstrate that ALMD algorithm can achieve its superiority in terms of the segmental SNR (segSNR), perceptual evaluation of speech Quality (PESQ), and short-time objective intelligibility measure (STOI), when compared with tested baseline algorithms.