27,972 research outputs found
Multi-Detector Multi-Component spectral matching and applications for CMB data analysis
We present a new method for analyzing multi--detector maps containing
contributions from several components. Our method, based on matching the data
to a model in the spectral domain, permits to estimate jointly the spatial
power spectra of the components and of the noise, as well as the mixing
coefficients. It is of particular relevance for the analysis of
millimeter--wave maps containing a contribution from CMB anisotropies.Comment: 15 pages, 7 Postscript figures, submitted to MNRA
Semi-blind speech-music separation using sparsity and continuity priors
In this paper we propose an approach for the problem of single channel source separation of speech and music signals. Our approach is based on representing each source's power spectral density using dictionaries and nonlinearly projecting the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the dictionary coefficients using penalty terms (or log-priors) in an optimization framework. We propose to use a novel coordinate descent technique for optimization, which nicely handles nonnegativity constraints and nonquadratic penalty terms. We use an adaptive Wiener filter, and spectral subtraction to reconstruct both of the sources from the mixture data after corresponding power spectral densities (PSDs) are estimated for each source. Using conventional metrics, we measure the performance of the system on simulated mixtures of single person speech and piano music sources. The results indicate that the proposed method is a promising technique for low speech-to-music ratio conditions and that sparsity and continuity priors help improve the performance of the proposed system
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor
typo
Neural networks and separation of Cosmic Microwave Background and astrophysical signals in sky maps
The Independent Component Analysis (ICA) algorithm is implemented as a neural
network for separating signals of different origin in astrophysical sky maps.
Due to its self-organizing capability, it works without prior assumptions on
the signals, neither on their frequency scaling, nor on the signal maps
themselves; instead, it learns directly from the input data how to separate the
physical components, making use of their statistical independence. To test the
capabilities of this approach, we apply the ICA algorithm on sky patches, taken
from simulations and observations, at the microwave frequencies, that are going
to be deeply explored in a few years on the whole sky, by the Microwave
Anisotropy Probe (MAP) and by the {\sc Planck} Surveyor Satellite. The maps are
at the frequencies of the Low Frequency Instrument (LFI) aboard the {\sc
Planck} satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical
radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic
diffuse emissions from thermal dust and synchrotron. We show that the ICA
algorithm is able to recover each signal, with precision going from 10% for the
Galactic components to percent for CMB; radio sources are almost completely
recovered down to a flux limit corresponding to , where
is the rms level of CMB fluctuations. The signal recovering
possesses equal quality on all the scales larger then the pixel size. In
addition, we show that the frequency scalings of the input signals can be
partially inferred from the ICA outputs, at the percent precision for the
dominant components, radio sources and CMB.Comment: 15 pages; 6 jpg and 1 ps figures. Final version to be published in
MNRA
Single channel speech music separation using nonnegative matrix factorization and spectral masks
A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization followed by masking to separate the mixed signal. In the training stage, NMF uses the training data to train a set of basis vectors for each source. These bases are trained using NMF in the magnitude spectrum domain. After observing the mixed signal, NMF is used to decompose its magnitude spectra into a linear combination of the trained bases for both sources. The decomposition results are used to build a mask, which explains the contribution of each source in the mixed signal. Experimental results show that using masks after NMF improves the separation process even when calculating NMF with fewer iterations, which yields a faster separation process
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