Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures
This work addresses a specific problem in speech signal processing for applications like audio enhancement, but it appears incremental as it builds on existing directional sparse filtering methods.
The paper tackled the challenge of separating unbalanced speech mixtures in blind source separation by proposing a directional sparse filtering algorithm that uses a weighted Lehmer mean to adapt to source imbalance, resulting in improved separation performance in real acoustic environments.
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.