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Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer by Koldovský, Zbyněk; Málek, Jiří; Čmejla, Jaroslav; O'Regan, Stephen is a scholarly article available to read on EtoBox.

What is Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer about?

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.

Author

Koldovský, Zbyněk; Málek, Jiří; Čmejla, Jaroslav; O'Regan, Stephen

Published

2024

Language

EN