Yao Rui, Zeng ZeQing, Zhu Ping
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
EURASIP J Adv Signal Process. 2016;2016(1):101. doi: 10.1186/s13634-016-0398-z. Epub 2016 Sep 22.
A priori signal-to-noise ratio (SNR) estimation and noise estimation are important for speech enhancement. In this paper, a novel modified decision-directed (DD) a priori SNR estimation approach based on single-frequency entropy, named DDBSE, is proposed. DDBSE replaces the fixed weighting factor in the DD approach with an adaptive one calculated according to change of single-frequency entropy. Simultaneously, a new noise power estimation approach based on unbiased minimum mean square error (MMSE) and voice activity detection (VAD), named UMVAD, is proposed. UMVAD adopts different strategies to estimate noise in order to reduce over-estimation and under-estimation of noise. UMVAD improves the classical statistical model-based VAD by utilizing an adaptive threshold to replace the original fixed one and modifies the unbiased MMSE-based noise estimation approach using an adaptive a priori speech presence probability calculated by entropy instead of the original fixed one. Experimental results show that DDBSE can provide greater noise suppression than DD and UMVAD can improve the accuracy of noise estimation. Compared to existing approaches, speech enhancement based on UMVAD and DDBSE can obtain a better segment SNR score and composite measure score, especially in adverse environments such as non-stationary noise and low-SNR.
先验信噪比（SNR）估计和噪声估计对于语音增强很重要。本文提出了一种基于单频熵的新型改进决策导向（DD）先验SNR估计方法，称为DDBSE。DDBSE用根据单频熵变化计算的自适应加权因子取代了DD方法中的固定加权因子。同时，提出了一种基于无偏最小均方误差（MMSE）和语音活动检测（VAD）的新噪声功率估计方法，称为UMVAD。UMVAD采用不同策略估计噪声，以减少噪声的高估和低估。UMVAD通过使用自适应阈值取代原始固定阈值来改进基于经典统计模型的VAD，并使用由熵计算的自适应先验语音存在概率取代原始固定概率来修改基于无偏MMSE的噪声估计方法。实验结果表明，DDBSE比DD能提供更大的噪声抑制，UMVAD能提高噪声估计的准确性。与现有方法相比，基于UMVAD和DDBSE的语音增强可以获得更好的分段SNR分数和综合测量分数，特别是在非平稳噪声和低SNR等不利环境中。
