Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

Tsalaile, T.; Naqvi, Syed Mohsen; Nazarpour, Kianoush; Sanei, Saeid; Chambers, Jonathon A.

doi:10.1109/icassp.2008.4517646

Cited by 22 publications

(13 citation statements)

References 14 publications

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“…Computer simulations were carried out to illustrate the performance of the proposed method, and were compared to the one proposed recently in [17], which is based on a fixed period of the SoI.…”

Section: Simulation Resultsmentioning

confidence: 99%

Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

Tsalaile

Sameni

Sanei

et al. 2009

IEEE Trans. Biomed. Eng.

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International audienceA novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results

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Section: Simulation Resultsmentioning

confidence: 99%

Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

Tsalaile

Sameni

Sanei

et al. 2009

IEEE Trans. Biomed. Eng.

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“…The visual information has, moreover, been shown to be useful for the cancellation of the two ambiguities of BSS algorithms [22,25,26].…”

Section: Introductionmentioning

confidence: 99%

Multimodal (audio–visual) source separation exploiting multi-speaker tracking, robust beamforming and time–frequency masking

Naqvi

Wang

Khan

et al. 2012

IET Signal Process.

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A novel multimodal source separation approach is proposed for physically moving and stationary sources which exploits a circular microphone array, multiple video cameras, robust spatial beamforming and time-frequency masking. The challenge of separating moving sources, including higher reverberation time (RT) even for physically stationary sources, is that the mixing filters are time varying; as such the unmixing filters should also be time varying but these are difficult to determine from only audio measurements. Therefore in the proposed approach, visual modality is used to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a three-dimensional tracker based on a Markov Chain Monte Carlo particle filter. The audio separation is performed by a robust least squares frequency invariant data-independent beamformer. The uncertainties in source localisation and direction of arrival information obtained from the 3D video-based tracker are controlled by using a convex optimisation approach in the beamformer design. In the final stage, the separated audio sources are further enhanced by applying a binary time-frequency masking technique in the cepstral domain. Experimental results show that using the visual modality, the proposed algorithm cannot only achieve performance better than conventional frequency-domain source separations algorithms, but also provide acceptable separation performance for moving sources.

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“…BSS is used to recover unknown sources from the observed mixtures with only limited assumptions such as the sources are independent. Many methods have been proposed to solve the BSS problem [2][3] [4][5] [6] and still much work is required to solve the cocktail party problem [7]. In frequency domain convolutive blind source separation (FDCBSS) the timedomain convolutive mixing is converted into a number of independent complex instantaneous mixing operations.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [11] proposed a solution to the BSS problem by incorporating a penalty function into the cost function (6). A penalty function is a non-negative function that is zero in the region where all constraints are satisfied (feasible region of solution space) and positive when any of the constraints are not satisfied (infeasible region of solution space).…”

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confidence: 99%

Evaluation of emerging frequency domain convolutive blind source separation algorithms based on real room recordings

Naqvi

Zhang

Tsalaile

et al. 2008

2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

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345-348Additional Information:• This is a conference paper [ c IEEE]. It is also available from: http://ieeexplore.ieee.org/ Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ABSTRACT This paper presents a comparative study of three of the emerging frequency domain convolutive blind source separation (FDCBSS) techniques i.e. convolutive blind separation of non-stationary sources due to Parra and Spence, penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources due to Wang et al. and a geometrically constrained multimodal approach for convolutive blind source separation due to Sanei et al.Objective evaluation is performed on the basis of signal to interference ratio (SIR), performance index (PI) and solution to the permutation problem. The results confirm that a multimodal approach is necessary to properly mitigate the permutation in BSS and ultimately to solve the cocktail party problem. In other words, it is to make BSS semiblind by exploiting prior geometrical information, and thereby providing the framework to find robust solutions for more challenging source separation with moving speakers.Index Terms-Frequency domain (BSS), geometrical constraints, orthogonal/nonorthogonal constraints, penalty functions, cocktail party problem and multimodal signal separation.

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Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

Cited by 22 publications

References 14 publications

Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

Multimodal (audio–visual) source separation exploiting multi-speaker tracking, robust beamforming and time–frequency masking

Evaluation of emerging frequency domain convolutive blind source separation algorithms based on real room recordings

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