arXiv subjects

Jiří Málek

Publications and source records attributed to Jiří Málek.

3 recordsLinked to original sources

arXiv2024

Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer

Non-Gaussianity-based Independent Vector Extraction leads to the famous one-unit FastICA/FastIVA algorithm when the likelihood function is optimized using an approximate Newton-Raphson algorithm under the orthogonality constraint. In this paper, we replace the constraint with the analytic form of the minimum variance distortionless beamformer (MVDR), by which a semi-blind variant of FastICA/FastIVA is obtained. The side information here is provided by a weighted covariance matrix replacing the noise covariance matrix, the estimation of which is a frequent goal of neural beamformers. The algorithm thus provides an intuitive connection between model-based blind extraction and learning-based extraction. The algorithm is tested in simulations and speaker ID-guided speaker extraction, showing fast convergence and promising performance.

eess.SP↗

arXiv2021

Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker

Recently, Constant Separating Vector (CSV) mixing model has been proposed for the Blind Source Extraction (BSE) of moving sources. In this paper, we experimentally verify the applicability of CSV in the blind extraction of a moving speaker and propose a new BSE method derived by modifying the auxiliary function-based algorithm for Independent Vector Analysis. Also, a piloted variant is proposed for the method with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using {\color{red} simulated as well as real-world acoustic conditions}. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of CSV as well as the improved convergence of the proposed algorithms.

eess.AS↗

arXiv2019

Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors

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 interest (SOI), which ensures that the method correctly extracts the utterance of the desired speaker. The pilot is based on the identification of a dominant speaker in the mixture using x-vectors. The properties of the x-vectors computed in the presence of cross-talk are experimentally analyzed. The proposed approach is verified in a scenario with a moving SOI, static interfering speaker, and environmental noise.

eess.AS↗