We extend the linear prediction-based dereverberation method called weighted prediction error (WPE). WPE optimizes a causal finite impulse response (FIR) filter that predicts the late reverberation components of an observed signal. However, by the multi-input/output inverse (MINT) theorem, in general, such FIR filters exist only when the number of sources isfewer than that of the microphones and no ambient noise exists. To mitigate the model error of WPE in adverse environments, we propose a mixture model of multiple WPEs in which the time frames are divided into clusters in each frequency bin and the WPE's causal FIR filter is optimized in each cluster. Experimental results show that our proposed method significantly improves the dereverberation performance of WPE when the noise level is high or the number of microphones is small.

中文翻译：

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基于加权预测误差模型混合的盲信号去混响

我们扩展了基于线性预测的去混响方法，称为加权预测误差（WPE）。WPE优化了因果有限脉冲响应（FIR）滤波器，该滤波器可预测观察到的信号的后期混响分量。然而，根据多输入/输出逆（MINT）定理，通常，仅当源的数量少于麦克风的数量并且不存在环境噪声时，才存在这样的FIR滤波器。为了减轻不利条件下WPE的模型误差，我们提出了多个WPE的混合模型，其中将时间范围划分为每个频点中的簇，并在每个簇中优化WPE的因果FIR滤波器。实验结果表明，该方法在噪声水平较高或麦克风数量较小时，可以显着提高WPE的去混响性能。