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Zbynek Koldovský

9 accepted papers

2024

Comparison Of Frequency-Fusion Mechanisms For Binaural Direction-Of-Arrival Estimation For Multiple Speakers

ICASSP 2024accepted

To estimate the direction of arrival (DOA) of multiple speakers with methods that use prototype transfer functions, frequency-dependent spatial spectra (SPS) are usually constructed. To make the DOA estimation robust, SPS from different frequencies can be combined. According to how the SPS are combi…

Cited by 0SourceScholar
2023

Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio

ICASSP 2023accepted

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constan…

Cited by 0SourceScholar
2021

Blind Extraction of Moving Audio Source in a Challenging Environment Supported by Speaker Identification Via X-Vectors

ICASSP 2021accepted

We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. The blind part of the method is based on independent vector extraction (IVE) and uses the recently proposed constant separating vector (CSV) mixing…

Cited by 8SourceScholar
2021

Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples

ICASSP 2021accepted

This paper is devoted to the recently proposed mixing model with constant separating vector (CSV) for Blind Source Extraction of moving sources using the FastDIVA algorithm, which is an extension of the famous FastICA and FastIVA for static mixtures. The benefits due to the CSV model and FastDIVA ar…

Cited by 0SourceScholar
2020

Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors

ICASSP 2020accepted

We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised by a pilot signal related to the source of in…

Cited by 0SourceScholar
2019

Extraction of Independent Vector Component from Underdetermined Mixtures through Block-wise Determined Modeling

ICASSP 2019accepted

We 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.…

Cited by 14SourceScholar
2019

Performance Bound for Blind Extraction of Non-gaussian Complex-valued Vector Component from Gaussian Background

ICASSP 2019accepted

Independent Vector Extraction aims at the joint blind source extraction of K dependent signals of interest (SOI) from K mixtures (one signal from one mixture). Similarly to Independent Component/Vector Analysis (ICA/IVA), the SOIs are assumed to be independent of the other signals in the mixture. Co…

Cited by 5SourceScholar
2016

Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling

ICASSP 2016accepted

The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previou…

Cited by 0SourceScholar