Combined approach of array processing and independent component analysis for blind separation of acoustic signals

Asano, Futoshi; Ikeda, Shiro; Ogawa, Mitsuhiro; Asoh, Hideki; Kitawaki, Nobuhiko

doi:10.1109/tsa.2003.809191

Cited by 111 publications

(65 citation statements)

References 14 publications

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“…It can be also anticipated that this optimal algorithm is the convolutive BSS algorithm Infomax [110], as will be corroborated by results given in chapter 7. The results presented in chapter 7 will justify the use of a convolutive model as well.…”

Section: A New Algorithm For Atrial Activity Estimation: Convolutive supporting

confidence: 60%

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Salort¹

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Supraventricular tachyarrhythmias, in particular Atrial Fibrillation (AF), are the most commonly cardiac diseases encountered in the routine clinical practice. The prevalence of AF is less than 1% among population under 60 years old, but it increases significantly among those over 70, approximating to 10% in those older than 80. Undergoing a sutained AF episode is related to a higher mortality ratio and to a rising probability of suffering thromboembolisms, myocardial infarction, and stroke. On the other hand, paroxysmal AF (PAF), which is characterized by its spontaneous termination, is frequently the precursor to sustained AF. This provokes a great interest among the scientific community in disclosing the mechanisms which lead to AF perpetuation or to spontaneous AF termination.The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of cardiac pathologies. In order to use the ECG as a tool for the AF study, the atrial activity (AA) must be separated from other cardioelectric signals. In this sense, Blind Source Separation (BSS) techniques are able to perform a multi-lead statistical analysis with the aim to obtain a set of independent sources that include the AA. When the BSS problem is tackled, it becomes necessary to consider a source mixing model near to the real mixing process in order to develop mathematical algorithms that solve the problem. A feasible model consists of assuming the linear mixture of sources. Within this linear mixing model it can be made the additional assumption of instantaneous mixture. This instantaneous linear mixing model is the one used in Independent Component Analysis (ICA). An alternative mixing model is considered by convolutive BSS (CBSS) algorithms, where a more realistic process in the generation of ECG leads is taken into account with delayed contributions of cardioelectric sources.In this thesis, a performance study of CBSS algorithms applied to AA extraction from ECG recordings has been carried out for the first time. With this aim, the most relevant CBSS algorithms have been compared with the instantaneous algorithm FastICA, the effectivity of which is extensively proved. This comparison will allow to know which CBSS algorithms are useful for AA extraction from ECG recordings of AF episodes. On the other hand, CBSS algorithms have the problem of requiring a minimum number of observed signals for their suitable vi ABSTRACT application. Here a new AA extraction algorithm is presented, which is based on the convolutive mixing model and solves the problem of lack of available leads from Holter ECG recordings. The high likeliness level between original and estimated AA, measured by different performance indicators, demonstrates the suitability of this new method for the AA extraction from Holter recordings and, furthermore, a higher robustness against noise of the convolutive mixing model is highlighted.The most common cause of undergoing an AF episode is attributed to the reentry mechanism, which consists o...

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Section: A New Algorithm For Atrial Activity Estimation: Convolutive supporting

confidence: 60%

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Salort¹

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“…For instance, HARK [7] localizes the sound sources before separation by using the number of sources. When independent component analysis (ICA), a well-known BSS method, is applied to the input signals, principal component analysis (PCA) is commonly used as preprocessing for ICA [8]. This is because the number of dimensions of ICA's input signals can be reduced.…”

Section: Robust Estimation Without Source Knowledgementioning

confidence: 99%

Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

Nagira

Otsuka

Okuno

2013

J AUDIO SPEECH MUSIC PROC.

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Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes auditory events in many environments. In daily environments, BSS suffers from many problems such as reverberation, a permutation problem in frequency-domain processing, and uncertainty about the number of sources in the observed mixture. While many conventional BSS methods resort to a cascaded combination of subprocesses, e.g., frequency-wise separation and permutation resolution, to overcome these problems, their outcomes may be affected by the worst subprocess. Our aim is to develop a unified framework to cope with these problems. Our method, called permutationfree infinite sparse factor analysis (PF-ISFA), is based on a nonparametric Bayesian framework that enables inference without a pre-determined number of sources. It solves BSS, SAD and the permutation problem at the same time. Our method has two key ideas: unified source activities for all the frequency bins and the activation probabilities of all the frequency bins of all the sources. Experiments were carried out to evaluate the separation performance and the SAD performance under four reverberant conditions. For separation performance in the BSS EVAL criteria, our method outperformed conventional complex ISFA under all conditions. For SAD performance, our method outperformed the conventional method by 5.9-0.5% in F-measure under the condition RT 20 = 30-600 [ms], respectively.

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“…For the permutation problem, spatial information, such as the direction-of-arrivals (DOA) of sources, can be estimated and used [13,14]. Another method utilizes the coherency of the mixing matrices in several adjacent frequencies [15]. For non-stationary sources such as speech, many methods exploit the dependency of separated signals across frequencies to solve the permutation problem [16,17].…”

Section: Introductionmentioning

confidence: 99%

Convolutive ICA for Audio Signals

Geravanchizadeh¹,

Hesam²

2012

Independent Component Analysis for Audio and Biosignal Applications

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Combined approach of array processing and independent component analysis for blind separation of acoustic signals

Cited by 111 publications

References 14 publications

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

Convolutive ICA for Audio Signals

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