基于凸壳卷积非负矩阵分解的多媒体通信语音增强

Dongxia Wang, Jie Cui, Jinghua Wang, Hua Tan, Ming Xu
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引用次数: 1

摘要

本文针对下一代多媒体通信系统提出了一种有效的语音增强方法。利用改进的凸壳卷积神经网络获得增强阶段的先验知识,减少了信息损失。针对其最优增益估计困难的问题,引入迭代算法对系数矩阵进行更新。在不同噪声环境下的实验结果表明,该算法能够显著降低信号失真,并同时提供比基准算法更好的增强性能,特别是在不利条件下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convex Hull Convolutive Non-negative Matrix Factorization Based Speech Enhancement For Multimedia Communication
In this paper, an effective speech enhancement method is proposed for the next generation multimedia communication system. The priori knowledge of the enhancement stage is obtained by the modified Convex Hull Convolutive NMF with less information loss. To deal with the difficulty of its optimal gain estimation, an iterative algorithm is then introduced to update the coefficient matrix. The experimental results under different types of noise environment show that the proposed algorithm can reduce the signal distortions dramatically, and provide better enhancement performance than the benchmark algorithms simultaneously, especially under adverse conditions.
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