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A robust and precise method for solving the permutation problem of frequencydomain blind source separation
 IEEE Trans. on Speech and Audio Processing 12
, 2004
"... This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation and the interfrequency correlation. We discuss the advantages and disadvantages of the two app ..."
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Cited by 116 (31 self)
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This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation and the interfrequency correlation. We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit their respective advantages. We also present a closed form formula to estimate the directions of source signals from a separating matrix obtained by ICA. Experimental results show that our method solved permutation problems almost perfectly for a situation that two sources were mixed in a room whose reverberation time was 300 ms. 1.
Fundamental Limitation Of Frequency Domain Blind Source Separation For Convolved Mixture Of Speech
 IEEE Trans. Speech Audio Process
, 2001
"... Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the po ..."
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Cited by 93 (16 self)
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Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the poor performance of BSS in a long reverberation environment. First, we show that it is useless to be constrained by the condition P << T, where T is the frame size of FFT and P is the length of a room impulse response. We also discuss the limitation of frequency domain BSS, by showing that the frequency domain BSS framework is equivalent to two sets of frequency domain adaptive beamformers.
A maximum likelihood approach to singlechannel source separation
 Journal of Machine Learning Research
, 2003
"... This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a priori sets of timedomain basis functions learned by independent component analysis (ICA) to the separation of mixed source signals observe ..."
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Cited by 47 (0 self)
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This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a priori sets of timedomain basis functions learned by independent component analysis (ICA) to the separation of mixed source signals observed in a single channel. The inherent time structure of sound sources is reflected in the ICA basis functions, which encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources.
A SURVEY OF CONVOLUTIVE BLIND SOURCE SEPARATION METHODS
 SPRINGER HANDBOOK ON SPEECH PROCESSING AND SPEECH COMMUNICATION
"... 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 realworld audio ..."
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Cited by 39 (0 self)
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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 realworld audio separation tasks.
Blind Source Separation Combining Independent Component Analysis and Beamforming
 EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2003:11, 1135–1146
, 2003
"... We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICAbased BSS section with estimation of the direction of arrival (D ..."
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Cited by 34 (6 self)
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We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICAbased BSS section with estimation of the direction of arrival (DOA) of the sound source, (2) null beamforming section based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve the lowconvergence problem through optimization in ICA. To evaluate its effectiveness, signalseparation and speechrecognition experiments are performed under various reverberant conditions. The results of the signalseparation experiments reveal that the noise reduction rate (NRR) of about 18 dB is obtained under the nonreverberant condition, and NRRs of 8 dB and 6 dB are obtained in the case that the reverberation times are 150 milliseconds and 300 milliseconds. These performances are superior to those of both simple ICAbased BSS and simple beamforming method. Also, from the speechrecognition experiments, it is evident that the performance of the proposed method in terms of the word recognition rates is superior to those of the conventional ICAbased
Natural gradient multichannel blind deconvolution and source separation using causal fir filters
 in Proc. IEEE ICASSP, May 2004
"... Practical gradientbased adaptive algorithms for multichannel blind deconvolution and convolutive blind source separation typically employ FIR filters for the separation system. Inadequate use of signal truncation within these algorithms can introduce steadystate biases into their converged solution ..."
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Cited by 33 (4 self)
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Practical gradientbased adaptive algorithms for multichannel blind deconvolution and convolutive blind source separation typically employ FIR filters for the separation system. Inadequate use of signal truncation within these algorithms can introduce steadystate biases into their converged solutions that lead to degraded separation and deconvolution performances. In this paper, we derive a natural gradient multichannel blind deconvolutionand source separation algorithm that mitigates these effects for estimating causal FIR solutions to these tasks. Numerical experiments verify the robust convergence performance of the new method both in multichannel blind deconvolution tasks for i.i.d. sources and in convolutive BSS tasks for acoustic sources, even for extremelyshort separation filters. 1.
Equivalence between frequency domain blind source separation and frequency domain adaptive beamforming, in
 Proc. of the IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP’02
"... Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., Adaptive Beamformers (ABF). The minimization of the offdiagonal components in the BSS update equation can be viewed as the minimization of the mean square error ..."
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Cited by 31 (14 self)
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Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., Adaptive Beamformers (ABF). The minimization of the offdiagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABF. The unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense if the two source signals are ideally independent. Therefore, we can conclude that the performance of the BSS is upper bounded by that of the ABF. This understanding clearly explains the poor performance of the BSS in a real room with long reverberation. 1.
A Blind Channel IdentificationBased TwoStage Approach to Separation and Dereverberation of Speech Signals in a Reverberant Environment,”
 IEEE Trans. on Speech and Audio Processing,
, 2005
"... ..."
Frequency Domain Blind MIMO System Identification Based on Second and Higher Order Statistics
 IEEE TRANS. SIGNAL PROCESSING
, 2001
"... We present a novel frequencydomain framework for the identification of a multipleinput multipleoutput (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on th ..."
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Cited by 29 (8 self)
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We present a novel frequencydomain framework for the identification of a multipleinput multipleoutput (MIMO) system driven by white, mutually independent, unobservable inputs. The system frequency response is obtained based on singular value decomposition (SVD) of a matrix constructed based on the powerspectrum and slices of polyspectra of the system output. By appropriately selecting the polyspectra slices, we can create a set of such matrices, each of which could independently yield the solution, or they could all be combined in a joint diagonalization scheme to yield a solution with improved statistical performance. The freedom to select the polyspectra slices allows us to bypass the frequencydependent permutation ambiguity that is usually associated with frequency domain SVD, while at the same time allows us compute and cancel the phase ambiguity. An asymptotic consistency analysis of the system magnitude response estimate is performed.
Measuring dependence of binwise separated signals for permutation alignment
 in frequencydomain BSS,” in IEEE International Symposium on Circuits and Systems (ISCAS’07
, 2007
"... Abstract — This paper presents a new method for grouping binwise separated signals for individual sources, i.e., solving the permutation problem, in the process of frequencydomain blind source separation. Conventionally, the correlation coefficient of separated signal envelopes is calculated to ju ..."
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Cited by 25 (8 self)
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Abstract — This paper presents a new method for grouping binwise separated signals for individual sources, i.e., solving the permutation problem, in the process of frequencydomain blind source separation. Conventionally, the correlation coefficient of separated signal envelopes is calculated to judge whether or not the separated signals originate from the same source. In this paper, we propose a new measure that represents the dominance of the separated signal in the mixtures, and use it for calculating the correlation coefficient, instead of a signal envelope. Such dominance measures exhibit dependence/independence more clearly than traditionally used signal envelopes. Consequently, a simple clustering algorithm with centroids works well for grouping separated signals. Experimental results were very appealing, as three sources including two coming from the same direction were separated properly with the new method. I.