79 research outputs found
Reverberant Audio Source Separation via Sparse and Low-Rank Modeling
The performance of audio source separation from underdetermined convolutive
mixture assuming known mixing filters can be significantly improved by using an
analysis sparse prior optimized by a reweighting l1 scheme and a wideband
datafidelity term, as demonstrated by a recent article. In this letter, we show
that the performance can be improved even more significantly by exploiting a
low-rank prior on the source spectrograms.We present a new algorithm to
estimate the sources based on i) an analysis sparse prior, ii) a reweighting
scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a
constrained form, and iv) a low-rank constraint on the source spectrograms.
Evaluation on reverberant music mixtures shows that the resulting algorithm
improves state-of-the-art methods by more than 2 dB of signal-to-distortion
ratio
Compressive Source Separation: Theory and Methods for Hyperspectral Imaging
With the development of numbers of high resolution data acquisition systems
and the global requirement to lower the energy consumption, the development of
efficient sensing techniques becomes critical. Recently, Compressed Sampling
(CS) techniques, which exploit the sparsity of signals, have allowed to
reconstruct signal and images with less measurements than the traditional
Nyquist sensing approach. However, multichannel signals like Hyperspectral
images (HSI) have additional structures, like inter-channel correlations, that
are not taken into account in the classical CS scheme. In this paper we exploit
the linear mixture of sources model, that is the assumption that the
multichannel signal is composed of a linear combination of sources, each of
them having its own spectral signature, and propose new sampling schemes
exploiting this model to considerably decrease the number of measurements
needed for the acquisition and source separation. Moreover, we give theoretical
lower bounds on the number of measurements required to perform reconstruction
of both the multichannel signal and its sources. We also proposed optimization
algorithms and extensive experimentation on our target application which is
HSI, and show that our approach recovers HSI with far less measurements and
computational effort than traditional CS approaches.Comment: 32 page
IMA for space : status and considerations
International audienceThis article aims at giving an overview the current status and potential perspectives, including the open points, for space applications of the Integrated Modular Avionics (IMA) concept defined and developed by the aeronautic industry. At first, a status will be made on the current of the on-board data-handling system for the spaceapplications, in particular the way CNES pushed the concept of platform and the way it has been spread by industry for non-CNES programs (under ESA contract, commercial market, international cooperation) : from SPOT earth observation to telecom satellites including scientific missions based on PROTEUS and MYRIADE platforms will be described. Then it will demonstrated to which extend the IMA concept is not yet directly applicable in the context of the space domain : technical (constraints/limitations on rad-hard processors, limited volume of embedded applications, mission-criticality of all on-board applications...), organisational constraints (ESA, national agencies and the various industry actors) and specific other space domain (market and associated investment budgets) will be detailed
A Robust Method to Count and Locate Audio Sources in a Stereophonic Linear Anechoic Mixture
International audienceWe propose a new method, called DEMIX Anechoic, to estimate the mixing conditions, i.e. number of audio sources plus attenuation and time delay of each sources, in an underdetermined anechoic mixture. The method relies on the assumption that in the neighborhood of some time-frequency points, only one source contributes to the mixture. Such time-frequency points, located with a local confidence measure, provide estimates of the attenuation, as well as the phase difference at some frequency, of the corresponding source. The time delay parameters are estimated, by a method similar to GCC-PHAT, on points having close attenuations. As opposed to DUET like methods, our method can estimate time-delay higher than only one sample. Experiments show that DEMIX Anechoic estimates, in more than 65% of the cases, the number of directions until 6 sources and outperforms DUET in the accuracy of the estimation by a factor of 10
Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
The underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently distributed random variable. Other approaches which are not blind assume a more structured model, like the Spectral Gaussian Mixture Models (Spectral-GMMs), thus exploiting statistical diversity of audio sources in the separation process. However, in this last approach, Spectral-GMMs are supposed to be learned from some training signals. In this paper, we propose a new approach for learning Spectral-GMMs of the sources without the need of using training signals. The proposed blind method significantly outperforms state-of-the-art approaches on stereophonic instantaneous music mixtures
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