基于感知掩蔽的重构非负矩阵分解单通道语音增强算法

Reconstructed NMF single channel speech enhancement algorithm based on perceptual masking

针对非负矩阵分解(NMF)语音增强算法在低信噪比(SNR)非稳定环境下存在噪声残留的问题,提出一种基于感知掩蔽的重构NMF(PM-RNMF)单通道语音增强算法。首先,将心理声学掩蔽特性应用于NMF语音增强算法中;其次,对不同频率位采用不同的掩蔽阈值,建立自适应感知掩蔽增益函数,通过阈值约束残余噪声能量和语音失真能量;最后,结合语音存在概率(SPP)进行感知增益修正,重构NMF算法,以此建立新的目标函数。仿真结果表明,在不同SNR的3种非稳定噪声环境下,与NMF、重构NMF(RNMF)、感知掩蔽深度神经网络(PM-DNN)算法相比,PM-RNMF算法的感知语音质量评估(PESQ)平均值分别提高了0.767、0.474、0.162,信源失真比(SDR)平均值分别提高了2.785、1.197、0.948。实验结果表明,无论是在低频还是高频PM-RNMF有更好的降噪效果。

Aiming at the problem of noise residual in Non-negative Matrix Factorization(NMF)speech enhancement algorithm in low Signal-to-Noise Ratio(SNR)unsteady environment,a Perceptual Masking-based reconstructed NMF(PMRNMF)single-channel speech enhancement algorithm was proposed.Firstly,psychoacoustic masking features were applied to NMF speech enhancement algorithms.Secondly,different masking thresholds were used for different frequencies to establish an adaptive perceptual masking gain function,and the residual noise energy and speech distortion energy were constrained by the thresholds.Finally,Speech Presence Probability(SPP)was combined to realize perceptual gain correction,the NMF algorithm was reconstructed and a new objective function was established.The simulation results show that under three kinds of unsteady noise environments with different SNR,the average Perceptual Evaluation of Speech Quality(PESQ)of PM-RNMF algorithm is improved by 0.767,0.474 and 0.162 respectively and the average Signal-to-Distortion Ratio(SDR)is increased by 2.785,1.197 and 0.948 respectively compared with NMF,RNMF(Reconstructive NMF)and PM-DNN(Perceptual Masking-Deep Neural Network)algorithms.Experimental results show that PM-RNMF has better noise reduction effect in both low frequency and high frequency.