基于子空间域的自适应小字典的语音增强
作者 : 未知

  关键词: 语音增强; 小字典; 子空间; K?SVD; OMP; 阈值
  中图分类号: TN912.35?34                       文献标识码: A                         文章编号: 1004?373X(2019)01?0046?05
  Abstract: Since the traditional speech enhancement algorithm of small dictionary has the problem of speech distortion for noise elimination, a speech enhancement algorithm based on adaptive small dictionary in subspace domain is proposed. A over?completed small dictionary is constructed by using the eigenvalues of noisy speech signal in the subspace domain to make the dictionary have perfect control mechanism for signal distortion and residual noise, which is possible to minimize the distortion of the signal while eliminating the noise. The [K] singular value decomposition (K?SVD) algorithm is used for sparse representation and dictionary updating for the noisy speech by means of over?complete small dictionary. The correlation threshold and energy threshold are set in orthogonal matching pursuit (OMP) algorithm to adaptively control the reconstruction and iteration times, and reduce the reconstruction time. The experimental results show that, in comparison with the algorithms given in literatures, the new algorithm under different noise backgrounds has higher SNR and PESQ, and can reduce the speech distortion and improve the speech quality.
  Keywords: speech enhancement; small dictionary; subspace domain; K?SVD; OMP; threshold 0  引  言
  语音增强[1]的目的就是尽可能地从噪声中提取出纯净语音信号。近年来,基于信号稀疏表示的语音增强算法受到广泛关注。稀疏表示[2]是指用尽可能少的非零系数来准确表示原始信号。由于使用冗余字典能很好地表示出在稀疏基上近似稀疏的语音信号,对于非稀疏的噪声不能进行表示,利用稀疏表示的这个特点能够有效去除信号中的噪声。K?SVD[3](K?Singular Value Decomposition)算法是最具代表性的一种稀疏表示算法。近年来,文献[4]提出一种基于频域上的小字典训练的语音增强算法,文献[5]提出一种基于Sparse K?SVD学习字典的语音增强方法,文献[6]提出一种基于自适应逼近残差的稀疏表示语音降噪方法。与这些基于频域的方法相比,信号子空间[7]可通过选取适当的拉格朗日乘子[ν],在抑制噪声的同时减少信号失真。因此,本文把字典训练方法应用于子空间域。而小字典易于进行奇异值分解,更能够体现出语音的局部特性,所以本文提出一种基于子空间域的自适应小字典的语音增强算法。在子空间域中用带噪语音信号的特征值构造过完备的小字典,然后将其作为初始字典,对带噪语音的特征值用K?SVD算法不断进行稀疏表示和字典更新。其中在OMP[8] (Orthogonal Matching Pursuit)算法中设置相关性阈值与能量阈值[9]来自适应控制重构阶段及迭代次数。
  实验结果表明,本文算法与原来的小字典语音增强算法相比,语音增强效果更好,且减少了运行时间,证实了新算法的有效性。
  注:本文通讯作者为贾海蓉。
  参考文献
  [1] YOU H, MA ZHIXIAN, WEI L I, et al. A speech enhancement method based on multi?task Bayesian compressive sensing [J]. IEICE transactions on information & systems, 2017(3): 557?559.
  [2] HSIEH C T, HUANG P Y, CHEN T W, et al. Speech enhancement based on sparse representation under color noisy environment [C]// 2016 IEEE International Symposium on Intelligent Signal Processing and Communication Systems. Nusa Dua: IEEE, 2016: 134?138.
  [3] RUBINSTEIN R, PELEG T, ELAD M. Analysis K?SVD: a dictionary?learning algorithm for the analysis sparse model [J]. IEEE transactions on signal processing, 2013, 61(3): 661?677.
  [4] 李轶南,张雄伟,曾理,等.基于小字典训练的语音增强算法[J].军事通信技术,2013,34(1):32?38.
  LI Yinan, ZHANG Xiongwei, ZENG Li, et al. Speech enhancement based on small dictionary training [J]. Journal of military communications technology, 2013, 34(1): 32?38.
  [5] 黄玲,李琳,王薇,等.基于Sparse K?SVD学习字典的语音增强方法[J].厦门大学学报(自然版),2014,53(1):36?40.
  HUANG Ling, LI Lin, WANG Wei, et al. Speech enhancement based on sparse K?SVD dictionary learning [J]. Journal of Xiamen University (natural science), 2014, 53(1): 36?40.
  [6] 周伟力,贺前华,王亚楼,等.基于自适应逼近残差的稀疏表示语音降噪方法[J].电子与信息学报,2017,39(2):309?315.
  ZHOU Weili, HE Qianhua, WANG Yalou, et al. Adapted stopping residue error based sparse representation for speech denoising [J]. Journal of electronics & information technology, 2017, 39(2): 309?315.
  [7] DAI X Z, YU B, DAI X H. An improved signal subspace algorithm for speech enhancement [C]// 2014 Conference on e?Business, e?Services and e?Society. Berlin: Springer, 2014: 104?114.
  [8] YANG H, HAO D, SUN H, et al. Speech enhancement using orthogonal matching pursuit algorithm [C]// 2014 IEEE International Conference on Orange Technologies. Xi’an: IEEE, 2014: 101?104.
  [9] 周伟栋,杨震,于云.改进的正交匹配追踪语音增强算法[J].信号处理,2016,32(3):287?295.
  ZHOU Weidong, YANG Zhen, YU Yun. Speech enhancement by using modified orthogonal matching pursuit algorithm [J]. Journal of signal processing, 2016, 32(3): 287?295.
  [10] 华志胜,付丽华.基于块分类和字典优化的K?SVD图像去噪研究[J].计算机工程与应用,2017,53(16):187?192.
  HUA Zhisheng, FU Lihua. K?SVD image denoising based on noisy image blocks classification and dictionary optimization [J]. Computer engineering & applications, 2017, 53(16): 187?192.
  [11] JOUNG J, SUN S. SCF: sparse channel?state?information feedback using Karhunen?Loève transform [C]// 2015 GLOBECOM Workshops. Austin: IEEE, 2015: 314?319.
  [12] NAKAYAMA K, HIGASHI S, HIRANO A. A noise estimation method based on improved VAD used in noise spectral suppression under highly non?stationary noise environments [C]// 2017 European Signal Processing Conference. Glasgow: IEEE, 2015: 2494?2498.

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