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RS-CAE-Based AR-Wiener Filtering and Harmonic Recovery for Speech Enhancement

机译:基于RS-CAE的AR维纳滤波和谐波恢复以增强语音

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By taking into account temporal correlation of speech feature. In this paper, a novel structure of convolutional Auto Encoder (CAE) was proposed. In this structure, the historical output of the CAE was fed into a CAE stack recurrently. We name this structure as Recurrent Stack Convolutional Auto Encoder (RS-CAE). In the training stage, the training feature maps of the RS-CAE comprise of log power spectrum (LPS) of noisy speech and an additional feature map derived from the LPS of the enhanced speech in the history. In this way, the temporal correlation is incorporated as much as possible in the RS-CAE. The training target is a concatenated vector of auto-regressive (AR) model parameters of speech and noise. At online stage, the LPS of noisy speech and the LPS of the enhanced speech from the history make up input feature maps together. The outputs of the RS-CAE are the AR model parameters of speech and noise, which are used to construct the AR-Wiener filter. Because the estimated AR model parameters are not completely accurate and some harmonics may be lost in the enhanced speech, the codebook-based harmonic recovery technique was proposed to reconstruct harmonic structure of the enhanced speech. The test results confirmed that the proposed method achieved better performance compared with some existing approaches.
机译:通过考虑语音特征的时间相关性。本文提出了一种新颖的卷积自动编码器(CAE)结构。在这种结构中,CAE的历史输出被循环馈入CAE堆栈。我们将此结构命名为递归堆栈卷积自动编码器(RS-CAE)。在训练阶段,RS-CAE的训练特征图包括嘈杂语音的对数功率谱(LPS)和历史中从增强语音的LPS得出的附加特征图。以这种方式,时间相关性被尽可能多地并入RS-CAE中。训练目标是语音和噪声的自回归(AR)模型参数的级联向量。在在线阶段,来自历史的嘈杂语音的LPS和增强语音的LPS一起构成了输入特征图。 RS-CAE的输出是语音和噪声的AR模型参数,用于构造AR-Wiener滤波器。由于估计的AR模型参数不能完全准确,增强语音中可能会丢失一些谐波,因此提出了一种基于码本的谐波恢复技术来重构增强语音的谐波结构。测试结果证实,与现有方法相比,该方法具有更好的性能。

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