Results 1  10
of
57
Independent Factor Analysis
 Neural Computation
, 1999
"... We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square no ..."
Abstract

Cited by 219 (9 self)
 Add to MetaCart
We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing, but also the general case where the number of mixtures differs from the number of sources and the data are noisy. IFA is a twostep procedure. In the first step, the source densities, mixing matrix and noise covariance are estimated from the observed data by maximum likelihood. For this purpose we present an expectationmaximization (EM) algorithm, which performs unsupervised learning of an associated probabilistic model of the mixing situation. Each source in our model is described by a mixture of Gaussians, thus all the probabilistic calculations can be performed analytically. In the second step, the sources are reconstructed from the observed data by an optimal nonlinear ...
Convolutive Blind Separation of NonStationary
"... Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the probl ..."
Abstract

Cited by 129 (3 self)
 Add to MetaCart
Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing crosscorrelations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multipath channel. In the same manner we can find an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have sufficient conditions to estimate underlying additive sensor noise powers. We show good performance in real room environments and demonstrate the algorithm's utility for automatic speech recognition.
An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals
 Neurocomputing
, 1998
"... In this paper we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals in contrast to most other major approaches to this problem. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the timefrequenc ..."
Abstract

Cited by 76 (4 self)
 Add to MetaCart
In this paper we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals in contrast to most other major approaches to this problem. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the timefrequency domain. We show some results of experiments with both artificially controlled data and speech data recorded in the real environment.
Maximum Likelihood Blind Source Separation: A ContextSensitive Generalization of ICA
 Advances in Neural Information Processing Systems 9
, 1997
"... In the square linear blind source separation problem, one must find a linear unmixing operator which can detangle the result x i (t) of mixing n unknown independent sources s i (t) through an unknown n \Theta n mixing matrix A(t) of causal linear filters: x i = P j a ij s j . We cast the problem ..."
Abstract

Cited by 73 (2 self)
 Add to MetaCart
In the square linear blind source separation problem, one must find a linear unmixing operator which can detangle the result x i (t) of mixing n unknown independent sources s i (t) through an unknown n \Theta n mixing matrix A(t) of causal linear filters: x i = P j a ij s j . We cast the problem as one of maximum likelihood density estimation, and in that framework introduce an algorithm that searches for independent components using both temporal and spatial cues. We call the resulting algorithm "Contextual ICA," after the (Bell and Sejnowski 1995) Infomax algorithm, which we show to be a special case of cICA. Because cICA can make use of the temporal structure of its input, it is able separate in a number of situations where standard methods cannot, including sources with low kurtosis, colored Gaussian sources, and sources which have Gaussian histograms. 1 The Blind Source Separation Problem Consider a set of n indepent sources s 1 (t); : : : ; s n (t). We are given n linearly d...
Blind Source Separation of Real World Signals
 Proc. ICNN
, 1997
"... We present a method to separate and deconvolve sources which have been recorded in real environments. The use of noncausal FIR filters allows us to deal with nonminimum mixing systems. The learning rules can be derived from different viewpoints such as information maximization, maximum likelihood an ..."
Abstract

Cited by 53 (8 self)
 Add to MetaCart
We present a method to separate and deconvolve sources which have been recorded in real environments. The use of noncausal FIR filters allows us to deal with nonminimum mixing systems. The learning rules can be derived from different viewpoints such as information maximization, maximum likelihood and negentropy which result in similar rules for the weight update. We transform the learning rule into the frequency domain where the convolution and deconvolution property becomes a multiplication and division operation. In particular, the FIR polynomial algebra techniques as used by Lambert present an efficient tool to solve true phase inverse systems allowing a simple implementation of noncausal filters. The significance of the methods is shown by the successful separation of two voices and separating a voice that has been recorded with loud music in the background. The recognition rate of an automatic speech recognition system is increased after separating the speech signals. 1 Introduct...
Ensemble learning for independent component analysis
 in Advances in Independent Component Analysis
, 2000
"... i Abstract This thesis is concerned with the problem of Blind Source Separation. Specifically we considerthe Independent Component Analysis (ICA) model in which a set of observations are modelled by xt = Ast: (1) where A is an unknown mixing matrix and st is a vector of hidden source components atti ..."
Abstract

Cited by 49 (2 self)
 Add to MetaCart
i Abstract This thesis is concerned with the problem of Blind Source Separation. Specifically we considerthe Independent Component Analysis (ICA) model in which a set of observations are modelled by xt = Ast: (1) where A is an unknown mixing matrix and st is a vector of hidden source components attime t. The ICA problem is to find the sources given only a set of observations. In chapter 1, the blind source separation problem is introduced. In chapter 2 the methodof Ensemble Learning is explained. Chapter 3 applies Ensemble Learning to the ICA model and chapter 4 assesses the use of Ensemble Learning for model selection.Chapters 57 apply the Ensemble Learning ICA algorithm to data sets from physics (a medical imaging data set consisting of images of a tooth), biology (data sets from cDNAmicroarrays) and astrophysics (Planck image separation and galaxy spectra separation).
Adaptive blind signal processingneural network approaches
 Proc. of the IEEE
, 1998
"... Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learnin ..."
Abstract

Cited by 43 (3 self)
 Add to MetaCart
Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.
Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm
 Neural Computation
, 1998
"... We derive a novel family of unsupervised learning algorithms for blind separation of mixed and convolved sources. Our approach is based on formulating the separation problem as a learning task of a spatiotemporal generative model, whose parameters are adapted iteratively to minimize suitable error ..."
Abstract

Cited by 39 (6 self)
 Add to MetaCart
We derive a novel family of unsupervised learning algorithms for blind separation of mixed and convolved sources. Our approach is based on formulating the separation problem as a learning task of a spatiotemporal generative model, whose parameters are adapted iteratively to minimize suitable error functions, thus ensuring stability of the algorithms. The resulting learning rules achieve separation by exploiting highorder spatiotemporal statistics of the mixture data. Different rules are obtained by learning generative models in the frequency and time domains, whereas a hybrid frequency/time model leads to the best performance. These algorithms generalize independent component analysis to the case of convolutive mixtures and exhibit superior performance on instantaneous mixtures. An extension of the relativegradient concept to the spatiotemporal case leads to fast and efficient learning rules with equivariant properties. Our approach can incorporate information about the mixing sit...
Separation of sound sources by convolutive sparse coding
 in Proc. ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, 2004. [Online] Available: http://journal.speech.cs.cmu.edu/SAPA2004
, 2004
"... An algorithm for the separation of sound sources is presented. Each source is parametrized as a convolution between a timefrequency magnitude spectrogam and an onset vector. The source model is able to represent several types of sounds, for example repetitive drum sounds and harmonic sounds with mo ..."
Abstract

Cited by 33 (4 self)
 Add to MetaCart
An algorithm for the separation of sound sources is presented. Each source is parametrized as a convolution between a timefrequency magnitude spectrogam and an onset vector. The source model is able to represent several types of sounds, for example repetitive drum sounds and harmonic sounds with modulations. An iterative algorithm is proposed for the estimation the parameters. The algorithm is based on minimizing the reconstruction error and the number of onsets. The number of onsets is minimized by applying the sparse coding scheme for onset vectors. A way of modeling the loudness perception of the human auditory system is proposed. The method compresses highenergy sources, and enables the separation of lowenergy sources which are perceptually significant. The algorithm is able to separate meaningful sources from realworld signals. Simulation experiments were carried out using mixtures of harmonic instruments. Demonstration signals are available at
Audio source separation of convolutive mixtures
 IEEE Trans. Speech Audio Process
, 2003
"... Abstract — The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is Blind Source Separation (BSS) using Independent Component Analysis (ICA). The recording environment is usually modelled as convolutive. ..."
Abstract

Cited by 31 (5 self)
 Add to MetaCart
Abstract — The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is Blind Source Separation (BSS) using Independent Component Analysis (ICA). The recording environment is usually modelled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.