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|Title:||A linear source recovery method for underdetermined mixtures of uncorrelated AR-model signals without sparseness||Authors:||Khong, Andy Wai Hoong
|Keywords:||Electrical and Electronic Engineering||Issue Date:||2013||Source:||Liu, B., Reju, V. G., & Khong, A. W. H. (2014). A linear source recovery method for underdetermined mixtures of uncorrelated AR-model signals without sparseness. IEEE transactions on signal processing, 62(19), 4947-4958.||Series/Report no.:||IEEE transactions on signal processing||Abstract:||Conventional sparseness-based approaches for instantaneous underdetermined blind source separation (UBSS) do not take into account the temporal structure of the source signals. In this work, we exploit the source temporal structure and propose a linear source recovery solution for the UBSS problem which does not require the source signals to be sparse. Assuming the source signals are uncorrelated and can be modeled by an autoregressive (AR) model, the proposed algorithm is able to estimate the source AR coefficients from the mixtures given the mixing matrix. We prove that the UBSS problem can be converted into a determined problem by combining the source AR model together with the original mixing equation to form a state-space model. The Kalman filter is then applied to obtain a linear source estimate in the minimum mean-squared error sense. Simulation results using both synthetic AR signals and speech utterances show that the proposed algorithm achieves better separation performance compared with conventional sparseness-based UBSS algorithms.||URI:||https://hdl.handle.net/10356/100623
|ISSN:||1053-587X||DOI:||10.1109/TSP.2014.2329646||Rights:||© 2013 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: [http://dx.doi.org/10.1109/TSP.2014.2329646].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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