113,224 research outputs found
Statistical single channel source separation
PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields
in signal processing and has various significant applications. Unlike conventional
SCSS methods which were based on linear instantaneous model, this research sets out
to investigate the separation of single channel in two types of mixture which is
nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear
SCSS in instantaneous mixture, this research proposes a novel solution based on a
two-stage process that consists of a Gaussianization transform which efficiently
compensates for the nonlinear distortion follow by a maximum likelihood estimator to
perform source separation. For linear SCSS in convolutive mixture, this research
proposes new methods based on nonnegative matrix factorization which decomposes a
mixture into two-dimensional convolution factor matrices that represent the spectral
basis and temporal code. The proposed factorization considers the convolutive mixing
in the decomposition by introducing frequency constrained parameters in the model.
The method aims to separate the mixture into its constituent spectral-temporal source
components while alleviating the effect of convolutive mixing. In addition, family of
Itakura-Saito divergence has been developed as a cost function which brings the
beneficial property of scale-invariant. Two new statistical techniques are proposed,
namely, Expectation-Maximisation (EM) based algorithm framework which
maximizes the log-likelihood of a mixed signals, and the maximum a posteriori
approach which maximises the joint probability of a mixed signal using multiplicative
update rules. To further improve this research work, a novel method that incorporates
adaptive sparseness into the solution has been proposed to resolve the ambiguity and
hence, improve the algorithm performance. The theoretical foundation of the proposed
solutions has been rigorously developed and discussed in details. Results have
concretely shown the effectiveness of all the proposed algorithms presented in this
thesis in separating the mixed signals in single channel and have outperformed others
available methods.Universiti Teknikal Malaysia Melaka(UTeM),
Ministry of Higher Education of Malaysi
Probabilistic Modeling Paradigms for Audio Source Separation
This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems
Compact source detection in multi-channel microwave surveys: from SZ clusters to polarized sources
In this paper we describe the state-of-the art status of multi-frequency
detection techniques for compact sources in microwave astronomy. From the
simplest cases where the spectral behaviour is well-known (i.e. thermal SZ
clusters) to the more complex cases where there is little a priori information
(i.e. polarized radio sources) we will review the main advances and the most
recent results in the detection problem.Comment: 13 pages, 4 figures. Accepted for publication in the Special Issue
"Astrophysical Foregrounds in Microwave Surveys" of the journal Advances in
Astronom
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
Sound Source Separation
This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2
Maximum likelihood, parametric component separation and CMB B-mode detection in suborbital experiments
We investigate the performance of the parametric Maximum Likelihood component
separation method in the context of the CMB B-mode signal detection and its
characterization by small-scale CMB suborbital experiments. We consider
high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping
low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency
channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1
to 10 micro-K per beam-size pixel. These are chosen to be representative of
some of the proposed, next-generation, bolometric experiments. We study the
residual foreground contributions left in the recovered CMB maps in the pixel
and harmonic domain and discuss their impact on a determination of the
tensor-to-scalar ratio, r. In particular, we find that the residuals derived
from the simulated data of the considered balloon-borne observatories are
sufficiently low not to be relevant for the B-mode science. However, the
ground-based observatories are in need of some external information to permit
satisfactory cleaning. We find that if such information is indeed available in
the latter case, both the ground-based and balloon-borne experiments can detect
the values of r as low as ~0.04 at 95% confidence level. The contribution of
the foreground residuals to these limits is found to be then subdominant and
these are driven by the statistical uncertainty due to CMB, including E-to-B
leakage, and noise. We emphasize that reaching such levels will require a
sufficient control of the level of systematic effects present in the data.Comment: 18 pages, 12 figures, 6 table
Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays
In the analysis of acoustic scenes, often the occurring sounds have to be
detected in time, recognized, and localized in space. Usually, each of these
tasks is done separately. In this paper, a model-based approach to jointly
carry them out for the case of multiple simultaneous sources is presented and
tested. The recognized event classes and their respective room positions are
obtained with a single system that maximizes the combination of a large set of
scores, each one resulting from a different acoustic event model and a
different beamformer output signal, which comes from one of several
arbitrarily-located small microphone arrays. By using a two-step method, the
experimental work for a specific scenario consisting of meeting-room acoustic
events, either isolated or overlapped with speech, is reported. Tests carried
out with two datasets show the advantage of the proposed approach with respect
to some usual techniques, and that the inclusion of estimated priors brings a
further performance improvement.Comment: Computational acoustic scene analysis, microphone array signal
processing, acoustic event detectio
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