Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity

Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain...
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Autor*in:	Seyyed Hamed Fouladi [verfasserIn] Ilangko Balasingham [verfasserIn] Kimmo Kansanen [verfasserIn] Tor Audun Ramstad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Blind source separation independent component analysis autoregressive conditional heteroscedasticity maximum likelihood Fisher’s information matrix

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 25336-25350
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:25336-25350

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2018.2823381

Katalog-ID:	DOAJ053861175

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allfieldsSound	10.1109/ACCESS.2018.2823381 doi (DE-627)DOAJ053861175 (DE-599)DOAJ1a69354fecae4f2cadf28c73607af03d DE-627 ger DE-627 rakwb eng TK1-9971 Seyyed Hamed Fouladi verfasserin aut Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source's samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals' dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Crameŕ Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB. Blind source separation independent component analysis autoregressive conditional heteroscedasticity maximum likelihood Fisher’s information matrix Electrical engineering. Electronics. Nuclear engineering Ilangko Balasingham verfasserin aut Kimmo Kansanen verfasserin aut Tor Audun Ramstad verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25336-25350 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25336-25350 https://doi.org/10.1109/ACCESS.2018.2823381 kostenfrei https://doaj.org/article/1a69354fecae4f2cadf28c73607af03d kostenfrei https://ieeexplore.ieee.org/document/8344792/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25336-25350
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topic_facet	Blind source separation independent component analysis autoregressive conditional heteroscedasticity maximum likelihood Fisher’s information matrix Electrical engineering. Electronics. Nuclear engineering
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callnumber-first	T - Technology
author	Seyyed Hamed Fouladi
spellingShingle	Seyyed Hamed Fouladi misc TK1-9971 misc Blind source separation misc independent component analysis misc autoregressive conditional heteroscedasticity misc maximum likelihood misc Fisher’s information matrix misc Electrical engineering. Electronics. Nuclear engineering Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity
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topic_title	TK1-9971 Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity Blind source separation independent component analysis autoregressive conditional heteroscedasticity maximum likelihood Fisher’s information matrix
topic	misc TK1-9971 misc Blind source separation misc independent component analysis misc autoregressive conditional heteroscedasticity misc maximum likelihood misc Fisher’s information matrix misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Blind source separation misc independent component analysis misc autoregressive conditional heteroscedasticity misc maximum likelihood misc Fisher’s information matrix misc Electrical engineering. Electronics. Nuclear engineering
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title	Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity
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title_full	Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity
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title_sort	blind source separation using temporal correlation, non-gaussianity and conditional heteroscedasticity
callnumber	TK1-9971
title_auth	Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity
abstract	Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source's samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals' dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Crameŕ Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB.
abstractGer	Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source's samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals' dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Crameŕ Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB.
abstract_unstemmed	Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source's samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals' dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Crameŕ Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB.
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title_short	Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity
url	https://doi.org/10.1109/ACCESS.2018.2823381 https://doaj.org/article/1a69354fecae4f2cadf28c73607af03d https://ieeexplore.ieee.org/document/8344792/ https://doaj.org/toc/2169-3536
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