用于混响背景语音分离的结构稀疏模型(Strutured sparisty model)方法-To further tackle the ambiguity

of the reflection ratios, we propose a novel formulation of the

reverberation model and estimate the absorption coefficients

through a convex optimization exploiting joint sparsity model

formulated upon spatio-spectral sparsity of concurrent speech

representation. The acoustic parameters are then incorporated

for separating individual speech signals through either structured

sparse recovery or inverse filtering the acoustic channels.

The experiments conducted on real data recordings of spatially

stationary sources demonstrate the effectiveness of the proposed

approach for speech separation and recognition.