In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix. We propose an algorithm based on the directional sparse filtering (DSF) framework that utilizes the Lehmer mean with learnable weights to adaptively account for source imbalance. Performance evaluation in multiple real acoustic environments show improvements in source separation compared to the baseline methods.

翻译：在盲源分隔语言信号方面,源谱的内在不平衡对依靠单一来源主导来估计混合矩阵的方法提出了挑战。我们建议采用基于方向性稀疏过滤框架的算法,即利用莱默(Lehmer)的平均值,以可学习的权重来适应性地说明源的不平衡。在多种真实的声学环境中的绩效评估显示,与基线方法相比,源的分离有所改善。