Browsing by Author "Janský, Jakub"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- ItemBLIND SOURCE SEPARATION USING INCOMPLETE DE-MIXING TRANSFORM WITH A PRECISE APPROACH FOR SELECTING CONSTRAINED SETS OF FREQUENCIES(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Janský, Jakub; Koldovský, ZbyněkThis paper presents a modification of the Natural Gradient algorithm for Independent Vector Analysis estimating incomplete de-mixing transform and performing its completion. Incomplete de-mixing transform is obtained when it is estimated only on subsets of most active frequencies of the sources. The transform is then completed using methods for sparse reconstruction. In previous works, the incomplete subset of frequencies was the same for all separated signals. In this paper, we propose a new approach in which the subsets are source-dependent. Experiments conducted on the CHiME-4 dataset show that the proposed approach improves the separation performance.
- ItemEXTRACTION OF INDEPENDENT VECTOR COMPONENT FROM UNDERDETERMINED MIXTURES THROUGH BLOCK-WISE DETERMINED MODELING(2019-05) Koldovský, Zbyněk; Málek, Jiří; Janský, JakubWe propose a new model for blind source extraction where the source of interest is assumed to be static while the background noise is dynamic. The model is determined within short blocks (the same number of sources as that of sensors), however, the noise subspace can be changing from block to block. We propose a gradient-based algorithm that jointly extracts an independent vector component from a set of mixtures obeying the model based on maximum quasi-likelihood principle. Simulations confirm the validity of the approach, and experiments with real-world recordings show promising results.