A hybrid algorithm for blind source separation of a convolutive mixture of three speech sources

Minhas, Shahab Faiz; Gaydecki, Patrick

doi:10.1186/1687-6180-2014-92

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…TheextendedInfomaxtechniqueisthealgorithmwhichistheextendedversionofInfomax.According to various sources (Lee, Girolami, & Sejnowski, 1999;Minhas & Gaydecki, 2014) it has been observedthattheextendedInfomaxcanseparateamplenumberofsourcesarisingfromavariety ofdistributions.InInfomaxonlyonelinearfunctionisusedinthelearningrulebutinExtended infomaxBellandT.Sejnowski,1995-97)haveconsideredtwolearningrulesforsuperGaussianand sub-Gaussiansignals.Herewehaveutilizedboththerulesforourstudy.…”

Section: Extended Information Maximization Approachmentioning

confidence: 99%

A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

Swaim¹,

Panda²,

Kabisatpathy

2019

International Journal of Rough Sets and Data Analysis

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In this article for the separation of Super Gaussian and Sub-Gaussian signals, we have considered the Multi-User Kurtosis(MUK), Infomax (Information Maximization) and Extended Infomax algorithms. For Extended Infomax we have taken two different non-linear functions and new coefficients and for Infomax we have taken a single non-linear function. We have derived MUK algorithm with stochastic gradient update iteratively using MUK cost function abided by a Gram-Schmidt orthogonalization to project on to the criterion constraint. Amongst the various standards available for measuring blind source separation, Cross-correlation coefficient and Kurtosis are considered to analyze the performance of the algorithms. An important finding of this study, as is evident from the performance table, is that the Kurtosis and Correlation coefficient values are the most favorable for the Extended Infomax algorithm, when compared with the others.

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Section: Extended Information Maximization Approachmentioning

confidence: 99%

A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

Swaim¹,

Panda²,

Kabisatpathy

2019

International Journal of Rough Sets and Data Analysis

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“…Also, the frequency domain (FD) implementation greatly reduces the computational complexity [17] that is associated with time domain implementation in channels with long impulse responses and has many other advantages [18]. Therefore, the frequency domain approach results in computational complexity reduction, while blind implementation ensured improved spectral efficiency and throughput [19][20][21][22]. Furthermore, FDSCS-MMA achieve lower mean square error (MSE) than both the normalized FD-modified constant modulus algorithm (NFDMMA) [23] and the popular constant modulus algorithm (CMA).…”

Section: Introductionmentioning

confidence: 99%

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

Akande

Iqbal

Zerguine

et al. 2016

EURASIP J. Adv. Signal Process.

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Single-carrier frequency division multiple access (SC-FDMA) has been adopted and employed as the standard in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) uplink multiple-access scheme. It offers comparable performance and complexity to orthogonal frequency multiple access scheme (OFDMA) with a lower peak to average power ratio (PAPR) offering power-efficient transmission and longer battery life to mobile terminals. However, due to its single-carrier nature, SC-FDMA performance degrades in channels with long impulse responses and becomes prohibitive to equalize when implemented in time domain (TD). Furthermore, of the seven SC-FDMA symbols in the LTE uplink slot, one full symbol is used for channel estimation leading to about 14 % throughput degradation. In this work, a novel frequency domain soft-constraint satisfaction multimodulus blind algorithm (FDSCS-MMA) is developed and proposed. The frequency domain approach results in computational complexity reduction while blind implementation ensured improved spectral efficiency and throughput. The algorithm convergence is further improved by normalization of each of the frequency bin in the weight update. Simulation results show superior performance of the developed algorithm over other blind algorithms.

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“…For this scenario, a number of different BSS techniques have been proposed for the case of two speech sources in both time domain and time-frequency domain [4,18,[20][21][22]. When the number of speech sources is more than two, the blind signal separation is more of a complicated and computational intense problem [23][24][25]. For this case, popular blind separation techniques are conducted to extract the desired source signal by finding a separating vector that maximizes the deterministic character (such as non-Gaussianity in ICA technique) of the extracted source signals [4,24,26,27].…”

Section: Introductionmentioning

confidence: 99%

Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

Dam¹,

Nordholm

2016

Vietnam J Comput Sci

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This paper presents a new blind speech separation algorithm using beamforming technique that is capable of extracting each individual speech signal from a mixture of three speech sources in a room. The speech separation algorithm utilizes the steered response power phase transform for obtaining a localization estimate for each individual speech source in the frequency domain. Based on those estimates each desired speech signal is extracted from the speech mixture using an optimal beamforming technique. To solve the permutation problem, a permutation alignment algorithm based on the mutual output correlation is employed to group the output signals into the correct sources from each frequency bin. Evaluations using real speech recordings in a room environment show that the proposed blind speech separation algorithm offers high interference suppression level whilst maintaining low distortion level for each desired signal.

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A hybrid algorithm for blind source separation of a convolutive mixture of three speech sources

Cited by 4 publications

References 28 publications

A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

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