On optimal source separation based on second and fourth order cumulants

Cardoso, Jean-François; Bose, Shourya; Friedlander, B.

doi:10.1109/ssap.1996.534852

Cited by 17 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A finite-sample problem is that does not reveal yet the true and , and the restriction might make the results less accurate. This problem was noted in [1], where optimal combinations of second-and fourth-order statistics are presented. In summary, we can say that JADE and ACMA are quite similar but differ in the following points.…”

Section: Connection To Jadementioning

confidence: 99%

Asymptotic properties of the algebraic constant modulus algorithm

Veen

2001

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. In this paper, we analyze its asymptotic properties and show that (unlike CMA) it converges to the Wiener beamformer when the number of samples or the signal-to-noise ratio (SNR) goes to infinity. We also sketch its connection to the related JADE algorithm and derive a version of ACMA that converges to a zero-forcing beamformer. This gives improved performance in applications that use the estimated mixing matrix, such as in direction finding.Index Terms-Array signal processing, blind beamforming, constant modulus algorithm, simultaneous diagonalization.

show abstract

Section: Connection To Jadementioning

confidence: 99%

Asymptotic properties of the algebraic constant modulus algorithm

Veen

2001

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…As mentioned in the introduction, tensor-based methods can combine the second-and fourth-order information using coupled decompositions. More elaborate weighting strategies, including statistically optimal weighting, have been studied in for instance [52], [53], [54], [55], [56]. With these approaches for instantaneous mixtures in mind, we consider which are suitable for convolutive mixtures.…”

Section: B Challengesmentioning

confidence: 99%

Coupled and Incomplete Tensors in Blind System Identification

Eeghem

Debals

Vervliet

et al. 2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Blind system identification (BSI) is an important problem in signal processing, arising in applications such as wireless telecommunications, biomedical signal processing and seismic signal processing. In the past decades, tensors have proven to be useful tools for these blind identification and separation problems. Most often, tensor-based methods based on fourthorder statistics are used, which have been studied extensively for independent component analysis and its convolutive extensions. However, these tensor-based methods have two main drawbacks: the accuracy is often limited by the estimation error of the statistics and the computation of these fourth-order statistics is time-intensive. In this paper, we propose to counter these drawbacks for BSI by coupling the fourth-order statistics with second-order statistics and by using incomplete tensors. By doing so, we can obtain more accurate results or obtain results in a much faster way.

show abstract

“…One more signal separation technique is SHIBBS (Shifted Block Blind Separation). The author of [28] stated that both JADE and SHIBBS separate the source signals similarly. Additionally the authors of [29] showed that for high-dimension data with large number of features the JADE was not very practical.…”

Section: ) Shibbsmentioning

confidence: 99%

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

Ostankovich

Prathap

Afanasyev

2018

2018 International Conference on Intelligent Systems (IS)

View full text Add to dashboard Cite

Human heartbeat can be measured using several different ways appropriately based on the patient condition which includes contact base such as measured by using instruments and non-contact base such as computer vision assisted techniques. Non-contact based approached are getting popular due to those techniques are capable of mitigating some of the limitations of contact-based techniques especially in clinical section. However, existing vision guided approaches are not able to prove high accurate result due to various reason such as the property of camera, illumination changes, skin tones in face image, etc. We propose a technique that uses video as an input and returns pulse rate in output. Initially, key point detection is carried out on two facial subregions: forehead and nose-mouth. After removing unstable features, the temporal filtering is applied to isolate frequencies of interest. Then four component analysis methods are employed in order to distinguish the cardiovascular pulse signal from extraneous noise caused by respiration, vestibular activity and other changes in facial expression. Afterwards, proposed peak detection technique is applied for each component which extracted from one of the four different component selection algorithms. This will enable to locate the positions of peaks in each component. Proposed automatic components selection technique is employed in order to select an optimal component which will be used to calculate the heartbeat. Finally, we conclude with comparison of four component analysis methods (PCA, FastICA, JADE, SHIBBS), processing face video datasets of fifteen volunteers with verification by an ECG/EKG Workstation as a ground truth.

show abstract

On optimal source separation based on second and fourth order cumulants

Cited by 17 publications

References 4 publications

Asymptotic properties of the algebraic constant modulus algorithm

Asymptotic properties of the algebraic constant modulus algorithm

Coupled and Incomplete Tensors in Blind System Identification

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

Contact Info

Product

Resources

About