基于改进谱平滑策略的IMCRA算法及其语音增强

发布时间：2018-04-27 05:32

  本文选题：噪声谱估计 + 最小统计算法(MS)　； 参考：《计算机工程与应用》2017年01期

【摘要】：噪声谱估计算法在单通道语音增强方法中起着重要作用,为了改善噪声谱估计算法对噪声的估计和更新能力,结合最小统计(MS)算法,对改进的基于控制的递归平均(IMCRA)噪声谱估计算法的递归平均参数进行改进,并用一阶递归的方式对平滑功率谱的最小值进行改进。采用谱减法对含噪语音信号作去噪处理,从客观和主观两方面对不同算法的性能进行评价,对比分析不同噪声不同信噪比下增强前后语音的分段信噪比(segSNR)、PESQ得分、MOS得分。实验结果表明,提出的方法能够更好地跟踪噪声信号变化,改善语音质量。
[Abstract]:Noise spectrum estimation algorithm plays an important role in single-channel speech enhancement methods. In order to improve the ability of noise spectrum estimation algorithm to estimate and update noise, the minimum statistical MSM algorithm is used to improve the performance of the noise spectrum estimation algorithm. The recursive average parameters of the improved noise spectrum estimation algorithm based on control are improved, and the minimum value of smooth power spectrum is improved by first order recursion. Spectral subtraction is used to de-noise the noisy speech signal. The performance of different algorithms is evaluated from objective and subjective aspects. The segmented SNR and PESQ scores of speech before and after enhancement are compared and analyzed in different noise and different signal-to-noise ratio (SNR). The experimental results show that the proposed method can better track the change of noise signal and improve the speech quality.
【作者单位】： 安徽大学计算机科学与技术学院;
【基金】：国家自然科学基金(No.61372137,No.61301295) 安徽省自然科学基金(No.1308085QF100)
【分类号】：TN912.35
，

本文编号：1809421