Probabilistic Combination Framework of Two Decision-Directed Algorithms for a Priori SNR Estimation

OU Shi-feng; ZHAO Yan-lei; SONG Peng; GAO Ying

doi:10.3969/j.issn.0372-2112.2020.08.020

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Probabilistic Combination Framework of Two Decision-Directed Algorithms for a Priori SNR Estimation

更新时间：2025-07-16

- Probabilistic Combination Framework of Two Decision-Directed Algorithms for a Priori SNR Estimation
- Acta Electronica Sinica Vol. 48, Issue 8, Pages: 1605-1614(2020)
- 作者机构：
  
  1. 烟台大学光电信息科学技术学院,山东,烟台,264005
  2. 烟台大学计算机与控制工程学院,山东,烟台,264005
  3. 烟台大学光电信息科学技术学院,山东,烟台,264005
  4. 烟台大学计算机与控制工程学院,山东,烟台,264005
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.08.020
  CLC： TN912.3
- Published Online：25 August 2020，
  
  Published：2020
- 稿件说明：
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OU Shi-feng, ZHAO Yan-lei, SONG Peng, et al. Probabilistic Combination Framework of Two Decision-Directed Algorithms for a Priori SNR Estimation[J]. Acta Electronica Sinica, 2020, 48(8): 1605-1614.
DOI：

OU Shi-feng, ZHAO Yan-lei, SONG Peng, et al. Probabilistic Combination Framework of Two Decision-Directed Algorithms for a Priori SNR Estimation[J]. Acta Electronica Sinica, 2020, 48(8): 1605-1614. DOI： 10.3969/j.issn.0372-2112.2020.08.020.

摘要

直接判决（DD，Decision-Directed）算法结构简单、音乐噪声抑制能力较好，是当前语音增强领域最为常用的先验信噪比估计方法.但该算法对于滑动因子的选取数值较为敏感，且估计性能要受到时延问题的限定.本文首先采用实际的语音和噪声数据，根据音乐噪声残留及输出语音失真两方面的评测标准对DD算法中滑动因子的取值问题进行了研究，通过数据分析给出了其较为明确的上下边界值；然后基于语音及噪声信号的复高斯分布模型，采用软判决技术对两个具有不同滑动因子的DD算法进行概率耦合，提出了一种具有双DD结构的先验信噪比估计算法.该算法可以充分结合两个具有不同特性DD算法的优点，在音乐噪声抑制及限制语音失真等方面均获得了较为理想的输出效果.多种噪声背景及输入信噪比条件下的仿真结果表明，相对于目前流行的几种先验信噪比估计算法，本文提出算法具有更为优良的估计性能.

Abstract

Due to the low computational complexity and acceptable ability in reducing musical noise effect

the decision-directed (DD) approach is widely used for estimating the a priori signal-noise-ratio (SNR) in many speech enhancement systems. However

the DD approach suffers from the problem of time delay and the performance is very sensitive to the fixed smoothing factor. Firstly

the performance of DD approach in musical noise reduction as well as speech distortion attenuation are analyzed using actual speech and noise data

and the boundary values of smoothing factors are presented in view of the analyzed results. Then

a novel algorithm is proposed

in which two DD approaches with different smoothing factors are probabilistically combined in an attempt to put together the best properties of them. The contribution of either DD approach to the combination is automatically adjusted in accordance with the speech absence probability

which can be computed using the complex Gaussian model and soft decision technique. Experiments are carried out in different noise and input SNR conditions

and the results demonstrate that the proposed algorithm can significantly outperform the popular methods for estimating the a priori SNR.

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