arXiv
Open Access
2020
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle
Andreas Brendel
Walter Kellermann
Abstrak
Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.
Topik & Kata Kunci
Penulis (2)
A
Andreas Brendel
W
Walter Kellermann
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2020
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- en
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- arXiv
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- Open Access ✓