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Equivariant Adaptive Source Separation
 IEEE Trans. on Signal Processing
, 1996
"... Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Eq ..."
Abstract

Cited by 385 (10 self)
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Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Equivariant Adaptive Separation via Independence) . The EASI algorithms are based on the idea of serial updating: this specific form of matrix updates systematically yields algorithms with a simple, parallelizable structure, for both real and complex mixtures. Most importantly, the performance of an EASI algorithm does not depend on the mixing matrix. In particular, convergence rates, stability conditions and interference rejection levels depend only on the (normalized) distributions of the source signals. Close form expressions of these quantities are given via an asymptotic performance analysis. This is completed by some numerical experiments illustrating the effectiveness of the proposed ap...
Algebraic Methods for Deterministic Blind Beamforming
, 1998
"... Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directionsofarrival. Progress in this area has been abundant over the p ..."
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Cited by 32 (6 self)
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Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directionsofarrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closedform expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, and generic algorithms that exploit them.
A.: Blind signal deconvolution by spatiotemporal decorrelation and demixing. Neural Networks for Signal Processing 7
, 1997
"... In this paper we present a simple efficient local unsupervised learning algorithm for online adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolut ..."
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Cited by 11 (4 self)
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In this paper we present a simple efficient local unsupervised learning algorithm for online adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolutive mixtures. Based on the existence of the inverse filter, we construct a twostage neural network which consists of blind equalization and source separation. In blind equalization stage, we employ antiHebbian learning in temporal domain for decorrelation. For blind separation, we can apply any existing algorithms. Extensive computer simulations confirm the validity and high performance of our proposed learning algorithm. 1
Adaptive Methods for Removing Camera Noise from Film Soundtracks
 McGill University
, 1998
"... One of the fundamental problems in signal processing is to enhance a signal which has been corrupted by an additive noise. In this thesis, the problem of deviating the effects of camera noise corrupting the dialog of a film soundtrack is examined. Two methods of noise reduction are investigated: ada ..."
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Cited by 7 (0 self)
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One of the fundamental problems in signal processing is to enhance a signal which has been corrupted by an additive noise. In this thesis, the problem of deviating the effects of camera noise corrupting the dialog of a film soundtrack is examined. Two methods of noise reduction are investigated: adaptive noise cancellation with a synthesized reference signal, and spectral subtraction. It is found that, due to the relatively low correlation between successive camera noise pulses, the adaptive noise cancellation approach is not effective at reducing camera noise. The spectral subtraction method is shown to reduce camera noise, but the process creates audible artifacts which can be very disturbing to the listener. To overcome this, new methods are proposed for reducing musical noise and time aliasing effects. The use of subbands and subframes is shown to sigmficantly improve the performance of the spectral subtraction algorithm by providing a better match of the noise reduction process to the noise. The performance is fuaher improved by incorporating a perceptual model into the spectral subtraction algorithm. The use of subbands, subframes, and a perceptual model allows the amount of processing applied to the signal to be minimized which in turn reduces the level of any artifacts which may result
Source Separation Based on Second Order Statistics  an Algebraic Approach
 In Proceedings of the VIII European Signal Processing Conference
, 1996
"... this paper is a blockmethod based on secondorder statistics of the measurement data only. The parameters of the inverse filter are to be found such that the resulting filtered output signals y 1 (t) and y 2 (t) have zero crosscovariance function. Assuming a certain filter structure, the resulting ..."
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Cited by 4 (3 self)
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this paper is a blockmethod based on secondorder statistics of the measurement data only. The parameters of the inverse filter are to be found such that the resulting filtered output signals y 1 (t) and y 2 (t) have zero crosscovariance function. Assuming a certain filter structure, the resulting conditions take the form of bilinear equations. The usual approach at this point is to set up a cost
EURASIP Journal on Applied Signal Processing 2003:11, 1091–1109 c ○ 2003 Hindawi Publishing Corporation Exploiting Acoustic Similarity of Propagating Paths for Audio Signal Separation
, 2003
"... Blind signal separation can easily find its position in audio applications where mutually independent sources need to be separated from their microphone mixtures while both room acoustics and sources are unknown. However, the conventional separation algorithms can hardly be implemented in real time ..."
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Blind signal separation can easily find its position in audio applications where mutually independent sources need to be separated from their microphone mixtures while both room acoustics and sources are unknown. However, the conventional separation algorithms can hardly be implemented in real time due to the high computational complexity. The computational load is mainly caused by either direct or indirect estimation of thousands of acoustic parameters. Aiming at the complexity reduction, in this paper, the acoustic paths are investigated through an acoustic similarity index (ASI). Then a new mixing model is proposed. With closely spaced microphones (5–10 cm apart), the model relieves the computational load of the separation algorithm by reducing the number and length of the filters to be adjusted. To cope with real situations, a blind audio signal separation algorithm (BLASS) is developed on the proposed model. BLASS only uses the secondorder statistics (SOS) and performs efficiently in frequency domain.
Blind Signal Separation/Deconvolution Using Recurrent Neural Networks
"... A new iterative learning algorithm based on 2D system theory using recurrent neural networks (RNNs) is presented for blind signal separation in this paper. The characteristics of convolutive (real) signals are matched by the structure of RNNs. The feedback paths in a RNN can memorise the past signal ..."
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A new iterative learning algorithm based on 2D system theory using recurrent neural networks (RNNs) is presented for blind signal separation in this paper. The characteristics of convolutive (real) signals are matched by the structure of RNNs. The feedback paths in a RNN can memorise the past signals (echoes) so that better separation can be achieved. The crosscorrelations of the outputs of the RNN are used as separation criterion. The experimental results for artificially mixed data and real multichannel recordings demonstrate the performances of the algorithm. 1.