Gain factor linear prediction based decision-directed method for the a priori SNR estimation

Wantao Zhang, S. Ou, Suojin Shen, Ying Gao
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引用次数: 1

Abstract

The performance of a noisy speech enhancement algorithm depends mainly on the accuracy of the a priori signal-to-noise ratio (SNR) estimate. The decision-directed (DD) algorithm for estimating the a priori SNR has received lots of attention due to its good performance in eliminating the musical noise and the low computational complexity. However, this algorithm has a serious problem in that the estimation result of the a priori SNR tracks the shape of the instantaneous SNR with one frame delay, which leads to the degraded quality of enhanced speech signal. To remove this drawback, our paper proposes a gain factor linear prediction based DD approach for the a priori SNR estimation. Firstly, we analyze the statistical dependence between the successive gain factors, and develop an adaptive scheme to predicate the current gain factor using the gain factors at previous fames. Then, the predicated gain factor at the current frame is mapped with the current noisy speech to compute the first component of the DD method. The advantage of our approach is that it does no longer consist of any priori information about the estimated a priori SNR at previous frame and effectively increases the tracking sensitivity to speech onsets. In the speech enhancement simulation experiments, the proposed method is shown to bring significant improvement as compared to the conventional DD method and its modified schemes.
基于增益因子线性预测的决策导向先验信噪比估计方法
噪声语音增强算法的性能主要取决于先验信噪比估计的准确性。决策导向(DD)先验信噪比估计算法因其良好的消噪性能和较低的计算复杂度而受到广泛关注。然而,该算法存在一个严重的问题,即先验信噪比的估计结果会在一帧延迟下跟踪瞬时信噪比的形状,从而导致增强语音信号的质量下降。为了消除这一缺点,本文提出了一种基于增益因子线性预测的DD方法用于先验信噪比估计。首先,我们分析了连续增益因子之间的统计相关性,并开发了一种自适应方案,利用前一帧的增益因子来预测当前增益因子。然后,将当前帧的预测增益因子与当前带噪声的语音进行映射,以计算DD方法的第一分量。该方法的优点在于它不再包含前一帧估计的先验信噪比的任何先验信息,并有效地提高了对语音发作的跟踪灵敏度。在语音增强仿真实验中,与传统的DD方法及其改进方案相比,该方法具有明显的改进效果。
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