117 research outputs found
Differential fast fixed-point algorithms for underdetermined instantaneous and convolutive partial blind source separation
This paper concerns underdetermined linear instantaneous and convolutive
blind source separation (BSS), i.e., the case when the number of observed mixed
signals is lower than the number of sources.We propose partial BSS methods,
which separate supposedly nonstationary sources of interest (while keeping
residual components for the other, supposedly stationary, "noise" sources).
These methods are based on the general differential BSS concept that we
introduced before. In the instantaneous case, the approach proposed in this
paper consists of a differential extension of the FastICA method (which does
not apply to underdetermined mixtures). In the convolutive case, we extend our
recent time-domain fast fixed-point C-FICA algorithm to underdetermined
mixtures. Both proposed approaches thus keep the attractive features of the
FastICA and C-FICA methods. Our approaches are based on differential sphering
processes, followed by the optimization of the differential nonnormalized
kurtosis that we introduce in this paper. Experimental tests show that these
differential algorithms are much more robust to noise sources than the standard
FastICA and C-FICA algorithms.Comment: this paper describes our differential FastICA-like algorithms for
linear instantaneous and convolutive underdetermined mixture
An adaptive stereo basis method for convolutive blind audio source separation
NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Improved Convolutive and Under-Determined Blind Audio Source Separation with MRF Smoothing
Convolutive and under-determined blind audio source separation from noisy recordings is a challenging problem. Several computational strategies have been proposed to address this problem. This study is concerned with several modifications to the expectation-minimization-based algorithm, which iteratively estimates the mixing and source parameters. This strategy assumes that any entry in each source spectrogram is modeled using superimposed Gaussian components, which are mutually and individually independent across frequency and time bins. In our approach, we resolve this issue by considering a locally smooth temporal and frequency structure in the power source spectrograms. Local smoothness is enforced by incorporating a Gibbs prior in the complete data likelihood function, which models the interactions between neighboring spectrogram bins using a Markov random field. Simulations using audio files derived from stereo audio source separation evaluation campaign 2008 demonstrate high efficiency with the proposed improvement
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
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