  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.
