Convolutive blind signal separation by estimating mixing channels in time domain

Won, Young-Gul; Lee, S.-Y.

doi:10.1049/el:20081453

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…The separation algorithms may use second order statistics or higher order statistics (HOS). Three separation algorithms are used through our work, independent component analysis (ICA), principle component analysis (PCA), and multi-user kurtosis (MUK) [13][14][15].…”

Section: Problem Formulationmentioning

confidence: 99%

Efficient Blind Signal Separation Algorithms for Wireless Multimedia Communication Systems

Ali,

Zahran,

El-Samie

et al. 2023

Preprint

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This paper studies the problem of multi-user blind signal separation (BSS) in wireless communications. The existing separation algorithms work on quadrature phase shift keying (QPSK). Through this work two proposed algorithms were presented to enhance the BSS performance. The first proposed algorithm uses wavelet denoising to remove noise from the received signals in time domain. It adopts different modulation techniques such as minimum shift keying (MSK), quadrature phase shift keying (QPSK), and Gaussian minimum shift keying (GMSK) then uses several BSS algorithms such as independent component analysis (ICA), principle component analysis (PCA), and multi user kurtosis (MUK) algorithms. The second proposed algorithm transfers the problem of BSS to transform domain and uses wavelet denoising to reduce noise effect on received mixture. BSS with Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) were investigated and compared to time domain performance. Minimum square error (MSE) and signal to noise ratio (SNR) were used as the evaluating metrics. Simulation results proved that in time domain, MUK with QPSK gives best performance and wavelet denoising was found to enhance the performance of BSS under all conditions. Signal separation in transform domain was found to give better performance than that in time domain due to the energy compaction process of these transforms and noise reduction due to their averaging effect.

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Section: Problem Formulationmentioning

confidence: 99%

Efficient Blind Signal Separation Algorithms for Wireless Multimedia Communication Systems

Ali,

Zahran,

El-Samie

et al. 2023

Preprint

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“…We can cite some cases such as an environmental noise, reverberation or just another person speaking at the same time with the target speaker. During the last decades, various methods of separating sounds have been proposed, such as the blind source separation (BSS) which allows the extraction of the unknown speech signals from the mixture signals with no a priori information about the mixed signals and the sources [1][2][3][4][5][6]. We mention also spectral subtraction [7], subspace analysis [8], hidden Markov modeling [9] and sinusoidal modeling [10].…”

Section: Introductionmentioning

confidence: 99%

Improved comb filter for the separation of the voiced speech signals in the case of two speakers

2019

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In this paper, we present a method for separating voiced sounds from a composite signal. This method is mainly based on the separation by modified comb filter. This filter is keyed to the average values of the estimated pitch. This estimation is performed through an autocorrelation of multi-scale product analysis to separate the effects of the source and the vocal tract. The "autocorrelation of the multi-scale product" method allows noise elimination and the apparition of a signal periodic structure. Peaks that appear are used to calculate the mean fundamental frequency of the target speaker which will be used in the corresponding comb filters to determine the target speaker contribution. After the subtraction of this contribution from the mixture, we obtain the intrusion speaker. This separation is validated by its application on Cooke database and a part of VCTK database and compared to recent methods as Wang-Brown, Hu-Wang, Zhang-Liu and Quan systems. Results confirm the performance of the proposed approach.

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“…The word blind means that there is no a priori information about the mixed signals and there sources. Several approaches have been proposed for blind signal separation (Sakai and Mitsuhashi 2008;Dam et al 2007Dam et al , 2008Manmontri and Naylor 2008;Curnew and How 2007;Szupiluk et al 2006;Moreau et al 2007;Won and Lee 2008). Some of these approaches depend on independent component analysis.…”

Section: Introductionmentioning

confidence: 99%

Blind separation of audio signals using trigonometric transforms and wavelet denoising

Hammam¹,

Elazm

El-Halawany

et al. 2010

Int J Speech Technol

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This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the discrete cosine transform (DCT) or the discrete sine transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Wavelet denoising of the noisy mixtures is recommended in this paper as a preprocessing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations, especially when the separation algorithm is preceded by a wavelet denoising step. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising step.

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Convolutive blind signal separation by estimating mixing channels in time domain

Cited by 10 publications

References 5 publications

Efficient Blind Signal Separation Algorithms for Wireless Multimedia Communication Systems

Efficient Blind Signal Separation Algorithms for Wireless Multimedia Communication Systems

Improved comb filter for the separation of the voiced speech signals in the case of two speakers

Blind separation of audio signals using trigonometric transforms and wavelet denoising

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