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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.09259 |
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| _version_ | 1866913428727136256 |
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| author | Koldovský, Zbyněk Málek, Jiří Čmejla, Jaroslav O'Regan, Stephen |
| author_facet | Koldovský, Zbyněk Málek, Jiří Čmejla, Jaroslav O'Regan, Stephen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer Koldovský, Zbyněk Málek, Jiří Čmejla, Jaroslav O'Regan, Stephen Signal Processing Sound Audio and Speech Processing 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. |
| title | Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer |
| topic | Signal Processing Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.09259 |