Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer
<|reference_start|>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.<|reference_end|>
@article{koldovský2024informed,
    title={Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer},
    author={Zbynv{e}k Koldovsk'y, Jiv{r}'i M'alek, Jaroslav v{C}mejla, and
  Stephen O'Regan},
    journal={arXiv preprint arXiv:2407.09259},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09259},
    primaryClass={eess.SP cs.SD eess.AS}
}