Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

Koldovsky, Zbynek; Kautský, Václav; Kounovsky, Tomas; Cmejla, Jaroslav

doi:10.23919/eusipco54536.2021.9616272

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…QuickIVE provides an alternative to FastDIVA. It is an algorithm whose convergence is slightly slower than that of FastDIVA; nevertheless, this algorithm sometimes appear to be more stable, as will be shown in Section V; see also [51] where QuickIVE is shown to take an advantage over FastDIVA in continuous on-line source extraction.…”

Section: F Relation To Previous Methodsmentioning

confidence: 95%

“…Similarly, the proposed QuickIVE is the extension of the method from [51] for L > 1. QuickIVE provides an alternative to FastDIVA.…”

Section: F Relation To Previous Methodsmentioning

confidence: 99%

“…Both algorithms are iterated according to the scheme given by Algorithm 1 until convergence prevails, where the same stopping rule (line 6 in Algorithm 1) is used as that in [12]. For the sake of consistency with our previous works [14], [51], the algorithms will be referred to as FastDIVA and QuickIVE, respectively.…”

Section: Learning Rulementioning

confidence: 99%

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Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms

Koldovsky¹,

Kautský²,

Tichavský³

2022

Preprint

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In this article, nonstationary mixing and source models are combined for developing new fast and accurate algorithms for Independent Component or Vector Extraction (ICE/IVE), one of which stands for a new extension of the well-known FastICA. This model allows for a moving sourceof-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian source model assuming tridiagonal covariance matrix structures is proposed. It is shown to be beneficial in the frequency-domain speaker extraction problem. The algorithms are verified in simulations. In comparison to the state-of-the-art algorithms, they show superior performance in terms of convergence speed and extraction accuracy.

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Section: F Relation To Previous Methodsmentioning

confidence: 95%

“…Similarly, the proposed QuickIVE is the extension of the method from [51] for L > 1. QuickIVE provides an alternative to FastDIVA.…”

Section: F Relation To Previous Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms

Koldovsky¹,

Kautský²,

Tichavský³

2022

Preprint

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show abstract

“…Additionally, it shows new potential and good separation performance in three environments: motion loudspeaker in a noisy environment, extraction of motion brain activity, and motion source. In 2021, Koldovský et al [ 40 ] proposed a new dynamic IVA algorithm. It is based on a mixed model in which the source-of-interest (SOI)-related mixing parameters are time-varying, and the separation parameters are time-invariant.…”

Section: Optimizing Iva Algorithm—optimizing Update Rulesmentioning

confidence: 99%

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Guo

Luo

2023

Sensors

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With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind source separation (BSS). BSS has been proven to have prominent applications in multichannel audio processing. For multichannel speech signals, independent component analysis (ICA) requires a certain statistical independence of source signals and other conditions to allow blind separation. independent vector analysis (IVA) is an extension of ICA for the simultaneous separation of multiple parallel mixed signals. IVA solves the problem of arrangement ambiguity caused by independent component analysis by exploiting the dependencies between source signal components and plays a crucial role in dealing with the problem of convolutional blind signal separation. So far, many researchers have made great contributions to the improvement of this algorithm by adopting different methods to optimize the update rules of the algorithm, accelerate the convergence speed of the algorithm, enhance the separation performance of the algorithm, and adapt to different application scenarios. This meaningful and attractive research work prompted us to conduct a comprehensive survey of this field. This paper briefly reviews the basic principles of the BSS problem, ICA, and IVA and focuses on the existing IVA-based optimization update rule techniques. Additionally, the experimental results show that the AuxIVA-IPA method has the best performance in the deterministic environment, followed by AuxIVA-IP2, and the OverIVA-IP2 has the best performance in the overdetermined environment. The performance of the IVA-NG method is not very optimistic in all environments.

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Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures—Algorithms

Koldovsky¹,

Kautský²,

Tichavský³

2022

IEEE Trans. Signal Process.

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Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

Cited by 3 publications

References 19 publications

Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms

Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures—Algorithms

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