Speech Segregation Using Constrained ICA

Lin, Qiu‐Hua; Zheng, Yongrui; Yin, Fuliang; Liang, Hualou

doi:10.1007/978-3-540-28647-9_124

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…In the previous work of Lin et al [22], the threshold n was initialized with a small value and increased gradually; but in this paper, the EGG signals can be considered as a same sort of signals, so it is reasonable to use an identical threshold for each energy function in all experiments. The identical thresholds were fixed according to one of the experiments and the same procedure in [22] with a minor modification, namely, the thresholds were initialized with large values and decreased gradually.…”

Section: Application On Eggmentioning

confidence: 99%

“…The identical thresholds were fixed according to one of the experiments and the same procedure in [22] with a minor modification, namely, the thresholds were initialized with large values and decreased gradually. The fixed thresholds, both 0.01 in this paper, were used in all other experiments.…”

Section: Application On Eggmentioning

confidence: 99%

“…More recently, an alternative approach, known as constrained ICA, incorporated some priori knowledge as constraints into the model in order to eliminate the indeterminacy of ICA and extract the desired components [18][19][20]. The priori knowledge can be temporal reference signals [21,22], as well as spatial constraints of the demixing matrix [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electrogastrogram extraction using independent component analysis with references

Cheng

Qian

2007

Neural Comput & Applic

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Electrogastrogram (EGG) is a noninvasive measurement of gastric myoelectrical activity cutaneously, which is usually covered by strong artifacts. In this paper, the independent component analysis (ICA) with references was applied to separate the gastric signal from noises. The nonlinear uncorrelatedness between the desired component and references was introduced as a constraint. The results show that the proposed method can extract the desired component corresponding to gastric slow waves directly, avoiding the ordering indeterminacy in ICA. Furthermore, the perturbations in EGG can be suppressed effectively. In summary, it can be a useful method for EGG analysis in research and clinical practice.

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Section: Application On Eggmentioning

confidence: 99%

Section: Application On Eggmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electrogastrogram extraction using independent component analysis with references

Cheng

Qian

2007

Neural Comput & Applic

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“…Meanwhile it has been shown that this constrained ICA learning algorithm converged approximately three times faster than the two-stage approaches [13]. At present, constrained ICA has been successfully used for some fields such as speech analysis [10], functional MRI data extracting [12][13][14][15] and so on. However, a resulting trade off of this method is that the convergence depends on a good choice of the learning rate.…”

Section: Introductionmentioning

confidence: 98%

A new constrained fixed-point algorithm for ordering independent components

Zhang

Guo

Shi

et al. 2008

Journal of Computational and Applied Mathematics

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Independent component analysis (ICA) aims to recover a set of unknown mutually independent components (ICs) from their observed mixtures without knowledge of the mixing coefficients. In the classical ICA model there exists ICs' indeterminacy on permutation and dilation. Constrained ICA is one of methods for solving this problem through introducing constraints into the classical ICA model. In this paper we first present a new constrained ICA model which composed of three parts: a maximum likelihood criterion as an objective function, statistical measures as inequality constraints and the normalization of demixing matrix as equality constraints. Next, we incorporate the new fixed-point (newFP) algorithm into this constrained ICA model to construct a new constrained fixed-point algorithm. Computation simulations on synthesized signals and speech signals demonstrate that this combination both can eliminate ICs' indeterminacy to a certain extent, and can provide better performance. Moreover, comparison results with the existing algorithm verify the efficiency of our new algorithm furthermore, and show that it is more simple to implement than the existing algorithm due to its advantage of not using the learning rate. Finally, this new algorithm is also applied for the real-world fetal ECG data, experiment results further indicate the efficiency of the new constrained fixed-point algorithm.

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“…As a result, the ICA-R algorithm extracts only the desired source signal, which is the closest one, in some sense, to the reference signal [20,21]. The ICA-R algorithm has been successfully used for speech analysis [19]. However, a drawback of ICA-R is that it is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%