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 covl score, especially in adverse environments such as non-stationary noise and low-SNR.

中文翻译：

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用于语音增强的先验SNR估计和噪声估计。

先验信噪比（SNR）估计和噪声估计对于语音增强很重要。本文提出了一种新的基于单频熵的改进的先导式信噪比估计决策方法DDBSE。DDBSE将DD方法中的固定加权因子替换为根据单频熵的变化而计算出的自适应加权因子。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）的噪声功率估计方法，即UMVAD。UMVAD采用不同的策略来估计噪声，以减少噪声的过高估计和过低估计。UMVAD通过利用自适应阈值代替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而不是原始固定阈值来修改基于无偏MMSE的噪声估计方法。实验结果表明，DDBSE可以提供比DD更大的噪声抑制，而UMVAD可以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段SNR分数和复合度量covl分数，尤其是在诸如非平稳噪声和低SNR的不利环境中。实验结果表明，DDBSE可以提供比DD更大的噪声抑制，而UMVAD可以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段SNR分数和复合度量covl分数，尤其是在诸如非平稳噪声和低SNR的不利环境中。实验结果表明，DDBSE可以提供比DD更大的噪声抑制，而UMVAD可以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段SNR分数和复合度量covl分数，尤其是在诸如非平稳噪声和低SNR的不利环境中。