稀疏正则非负矩阵分解的语音增强算法

摘要

In order to improve the robustness of Non-negative Matrix Factorization (NMF) algorithm for speech enhancement in different background noises,a speech enhancement algorithm based on Sparsity-regularized Robust NMF (SRNMF) was proposed,which takes into account the noise effect of data processing,and makes sparse constraints on the coefficient matrix to get better speech characteristics of the decomposed data.First,the prior dictionary of the amplitude spectrum of speech and noise were learned and the joint dictionary matrix of speech and noise were constructed.Then,the SRNMF algorithm was used to update the coefficient matrix of the amplitude spectrum with noise in the joint dictionary matrix.Finally,the original pure speech was reconstructed,and enhanced.The speech enhancement performance of the SRNMF algorithm in different environmental noise was analyzed through simulation experiments.Experimental results show that the proposed algorithm can effectively weaken the influence of noise changes on performance under non-stationary environments and low Signal-to-Noise Ratio (SNR) (＜ 0 dB),it not only has about 1-1.5 magnitudes improvement in Source-to-Distortion Ratio (SDR) scores,but also is faster than other algorithms,which makes the NMF-based speech enhancement algorithm more practical.%对于非负矩阵分解的语音增强算法在不同环境噪声的鲁棒性问题,提出一种稀疏正则非负矩阵分解(SRNMF)的语音增强算法.该算法不仅考虑到数据处理时的噪声影响,而且对系数矩阵进行了稀疏约束,使其分解出的数据具有较好的语音特征.该算法首先在对语音和噪声的幅度谱先验字典矩阵学习的基础上,构建联合字典矩阵,然后更新带噪语音幅度谱在联合字典矩阵下的系数矩阵,最后重构原始纯净语音,实现语音增强.实验结果表明,在非平稳噪声和低信噪比(小于0 dB)条件下,该算法较好地削弱了噪声的变化对算法性能的影响,不仅有较高的信源失真率(SDR),提高了1～1.5个数量级,而且运算速度也有一定程度的提高,使得基于非负矩阵分解的语音增强算法更实用.