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Online mvbf adaptation under diffuse noise environments with mimo based noise pre-filtering

机译:使用基于mimo的噪声预滤波在弥散噪声环境下进行在线mvbf自适应

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摘要

A noise-robust MVBF adaptation technique under diffuse noise environments is proposed. The proposed method is compatible with online adaptation and robustness against diffuse noise by combining a semi-online diffuse noise reduction and an online MVBF adaptation technique with sparseness assumption of speech sources. The online sparseness based MVBF adaptation is sensitive to diffuse noise, because diffuse noise is not sparse. However, by using diffuse noise pre-filtering based on local Gaussian modeling which can be regarded as an optimized MIMO(Multi-Input Multi-Output) diffuse noise reduction method from the probabilistic perspective, sparseness of the microphone input signal into the latter part is expected to be improved. The proposed method is evaluated by using speech signal under diffuse noise environments, and the proposed method can reduce more noise source with less distortion than the conventional online sparseness based MVBF adaptation.
机译:提出了一种在噪声环境下的鲁棒MVBF自适应技术。通过将半在线扩散噪声降低和在线MVBF自适应技术与语音源的稀疏假设相结合,该方法与在线自适应和针对散射噪声的鲁棒性兼容。基于在线稀疏的MVBF适应对弥散噪声敏感,因为弥散噪声不稀疏。然而,通过使用基于局部高斯模型的扩散噪声预滤波,从概率的角度来看,可以将其视为一种优化的MIMO(多输入多输出)扩散噪声降低方法,麦克风输入信号的稀疏性是后者。有望得到改善。该方法是在弥散噪声环境下通过语音信号进行评估的,与传统的基于在线稀疏的MVBF自适应方法相比,该方法可以减少失真,减少噪声源。

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