On the Suppression of Noise from a Fast Moving Acoustic Source using Multimodality

International audienceThe problem of cancelling the noise from a moving acoustic source in outdoor environment is investigated in this paper. By making use of the known instantaneous location of the moving source (provided by a second modality), we propose a time-domain method for removing the noise from a moving source in a mixture of acoustic sources. The proposed method consists in resampling the mixed data recorded at a reference sensor, and by linearly combining the resampled data and the non-resampled data of the others sensor to cancel the undesired source. Simulation on synthetic data show the effectiveness and the usefulness of the proposed method

Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone

International audienceWe address the problem of Blind Source Separation (BSS) when the hidden sources are Nonnegative (N-BSS). In this case, the scatter plot of the mixed data is contained within the simplicial cone generated by the columns of the mixing matrix. The proposed method, termed SCSA-UNS for Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources, aims at estimating the mixing matrix and the sources by fitting a Minimum Aperture Simplicial Cone (MASC) to the cloud of mixed data points. SCSA-UNS is evaluated on both independent and correlated synthetic data and compared to other N-BSS methods. Simulations are also performed on real Liquid Chromatography-Mass Spectrum (LC-MS) data for the metabolomic analysis of a chemical sample, and on real dynamic Positron Emission Tomography (PET) images, in order to study the pharmacokinetics of the [18F]-FDG (FluoroDeoxyGlucose) tracer in the brain

A robust geometrical method for blind separation of noisy mixtures of non-negatives sources

International audienceRecently, we proposed an effective geometrical method for separating linear instantaneous mixtures of non-negative sources, termed Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources (SCSA-UNS). The latter method operates in noiseless case, and estimates the mixing matrix and the sources by finding the minimum aperture simplicial cone, containing the scatter plot of mixed data. In this paper, we propose an extension of SCSA-UNS, to tackle the noisy mixtures, in the case where the sparsity degrees of the sources are known a priori. The idea is to progressively eliminate, the noisy mixed data points which are likely to significantly modify the scatter plot of noiseless mixed data and to lead to a bad estimation of the mixing matrix and the sources. Simulations on synthetic data show the effectiveness of the proposed method