Directional Sparse Filtering Using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures
TL
TL;DR
An algorithm based on the directional sparse filtering (DSF) framework that utilizes the Lehmer mean with learnable weights to adaptively account for source imbalance is proposed.
Abstract
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.
References20 items
1
Directional Sparse Filtering for Blind Estimation of Under-Determined Complex-Valued Mixing Matrices
2
Learning complex-valued latent filters with absolute cosine similarity
3
An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers
4
Schur-Convexity for Lehmer mean of n variables
5