Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138107
Title: Directional sparse filtering for blind estimation of under-determined complex-valued mixing matrices
Authors: Nguyen, Anh Hai Trieu
Reju, Vaninirappuputhenpurayil Gopalan
Khong, Andy W. H.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Nguyen, A. H. T., Reju, V. G., & Khong, A. W. H. (2020). Directional sparse filtering for blind estimation of under-determined complex-valued mixing matrices. IEEE Transactions on Signal Processing, 68, 1990- 2003. doi:10.1109/TSP.2020.2979550
Project: SLE-RP5
Journal: IEEE Transactions on Signal Processing
Abstract: We propose an algorithm that exploits the benefits of sparse filtering and directional clustering when estimating under-determined mixing matrix from mixtures of sufficiently sparse sources. To express the direction of each sample by only a few vectors in which one vector is more dominant than the remaining ones, we propose to minimize the power mean of the magnitude-squared cosine distances between the estimated mixing matrix and the data. For the special case of estimating determined mixing matrix, we derive a stability condition for methods based on the magnitude-squared cosine metric. Our stability condition shows that the proposed approach, K-hyperlines, and sparse filtering can recover the invertible mixing matrix when the sources are i.i.d. super-Gaussian. Simulations using both synthetic data and recorded speech mixtures show that the proposed algorithm outperforms existing algorithms with lower computational complexity.
URI: https://hdl.handle.net/10356/138107
ISSN: 1053-587X
DOI: 10.1109/TSP.2020.2979550
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2020.2979550
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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