Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model

Benxu Liu; V. G. Reju; Andy W.H. Khong

doi:10.1109/ICASSP.2013.6637613

Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model

Benxu Liu, V. G. Reju, Andy W.H. Khong

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this work, we exploit, in addition to sparseness, the temporal structure of the source signals to address the problem of underdetermined blind source separation. To achieve good separation performance and reduction of artifacts, a two-stage algorithm is proposed. In the first stage, the auto-regressive (AR) coefficients of the source signals are estimated using partially separated sources that have been derived from conventional sparseness-based algorithm. In the second stage, the AR model is combined with the mixing equation to form a state-space model. This model is subsequently solved using the Kalman filter in order to obtain the refined source estimate. Simulation results show the effectiveness of proposed sparseness-based AR-Kalman (SPARK) algorithm compared to the conventional sparseness-based algorithms.

Original language	English
Title of host publication	2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages	81-85
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP.2013.6637613
Publication status	Published - Oct 18 2013
Externally published	Yes
Event	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada Duration: May 26 2013 → May 31 2013

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Conference

Conference	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/Territory	Canada
City	Vancouver, BC
Period	5/26/13 → 5/31/13

ASJC Scopus Subject Areas

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

autoregressive model
instantaneous mixing
state-space model
Underdetermined blind source separation

Access to Document

10.1109/ICASSP.2013.6637613

Cite this

Liu, B., Reju, V. G., & Khong, A. W. H. (2013). Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 81-85). Article 6637613 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6637613

Liu, Benxu ; Reju, V. G. ; Khong, Andy W.H. / Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model. 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. pp. 81-85 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "In this work, we exploit, in addition to sparseness, the temporal structure of the source signals to address the problem of underdetermined blind source separation. To achieve good separation performance and reduction of artifacts, a two-stage algorithm is proposed. In the first stage, the auto-regressive (AR) coefficients of the source signals are estimated using partially separated sources that have been derived from conventional sparseness-based algorithm. In the second stage, the AR model is combined with the mixing equation to form a state-space model. This model is subsequently solved using the Kalman filter in order to obtain the refined source estimate. Simulation results show the effectiveness of proposed sparseness-based AR-Kalman (SPARK) algorithm compared to the conventional sparseness-based algorithms.",

keywords = "autoregressive model, instantaneous mixing, state-space model, Underdetermined blind source separation",

author = "Benxu Liu and Reju, \{V. G.\} and Khong, \{Andy W.H.\}",

year = "2013",

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booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",

note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",

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Liu, B, Reju, VG & Khong, AWH 2013, Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model. in 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings., 6637613, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 81-85, 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, 5/26/13. https://doi.org/10.1109/ICASSP.2013.6637613

Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model. / Liu, Benxu; Reju, V. G.; Khong, Andy W.H.
2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. p. 81-85 6637613 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model

AU - Liu, Benxu

AU - Reju, V. G.

AU - Khong, Andy W.H.

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N2 - In this work, we exploit, in addition to sparseness, the temporal structure of the source signals to address the problem of underdetermined blind source separation. To achieve good separation performance and reduction of artifacts, a two-stage algorithm is proposed. In the first stage, the auto-regressive (AR) coefficients of the source signals are estimated using partially separated sources that have been derived from conventional sparseness-based algorithm. In the second stage, the AR model is combined with the mixing equation to form a state-space model. This model is subsequently solved using the Kalman filter in order to obtain the refined source estimate. Simulation results show the effectiveness of proposed sparseness-based AR-Kalman (SPARK) algorithm compared to the conventional sparseness-based algorithms.

AB - In this work, we exploit, in addition to sparseness, the temporal structure of the source signals to address the problem of underdetermined blind source separation. To achieve good separation performance and reduction of artifacts, a two-stage algorithm is proposed. In the first stage, the auto-regressive (AR) coefficients of the source signals are estimated using partially separated sources that have been derived from conventional sparseness-based algorithm. In the second stage, the AR model is combined with the mixing equation to form a state-space model. This model is subsequently solved using the Kalman filter in order to obtain the refined source estimate. Simulation results show the effectiveness of proposed sparseness-based AR-Kalman (SPARK) algorithm compared to the conventional sparseness-based algorithms.

KW - autoregressive model

KW - instantaneous mixing

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Liu B, Reju VG, Khong AWH. Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. p. 81-85. 6637613. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2013.6637613

Underdetermined instantaneous blind source separation of sparse signals with temporal structure using the state-space model

Abstract

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ASJC Scopus Subject Areas

Keywords

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