目录 第一章简介
1.1 谱减法
1.2 变换带宽与信噪比加权法
1.3 论文接下来的安排
第二章 算法推导
2.1 理论与公式
2.2 非约束参数估计器推导过程
2.3 约束参数估计器推导过程
第三章 变换带宽法与信噪比加权法
3.1 变换带宽法
3.2 信噪比加权法
第四章 开发实现
第五章 各种方法对比讨论
第六章 结论
第七章 专用名词解释 参考资料 [1] J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proc. IEEE, vol. 67, pp. 1586–1604, Dec.1979.
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论文摘要 谱减法是消除噪音的经典算法，它有多个版本的改进方法，原始谱减法和它的各种改进方法可以归纳为一个通用谱减法参数公式，本文从这个公式出发，运用最小平均方误差(MMSE)的方法进行参数优化，得到约束的短时语音谱估计器和非约束的短时语音谱估计器，它们在保持谱减法计算简单的优点的同时更好的消除了噪音。这两种估计器不同于以往的估计器。以往的都是非统计性的估计器，是靠经验来调整参数的。而本文提出的估计器是统计性估计器，是通过统计来调整估计器的参数，使之达到更好的效果。在此基础上，本文又进一步提出两种修改办法：变换带宽法(Change Band-Width)和信噪比加权法(SNR Waiting)，研究谱减法通用公式的优化。以往的消除噪音的算法都是在某个单独的确定的频带划分算法上进行的，算法割裂了相邻频带之间可能存在的联系，变换带宽法(CBW)可以克服这个问题。信噪比加权法(SNRW)用于谱减法完成后的信号提升，尽可能的使得强信号更强，弱信号更弱，从而使语音信号得到进一步巩固。本文提出的算法最后在白色高斯噪音和粉红噪音(Pink)下测试得到满意的效果，加入100dB的背景噪音后污染得十分模糊的频谱图，经过消除噪音后，频谱图与未加噪音前几乎完全一样。