Comparative Study of QPSO and other methods in Blind Source Separation

Salman, Hussein; Abbas, Nidaa A.

doi:10.1088/1742-6596/1804/1/012097

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…According to the results of previous studies [ 1 , 3 , 23 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ], the quality of speech blind source separation could be improved by swarm intelligence algorithms, but it is rare to find a study about multi-groups with random linear mixed signals. In their studies, the quality, convergence speed, and convergence accuracy of BSS were significantly improved by enhancing the swarm intelligence optimization algorithm.…”

Section: Discussionmentioning

confidence: 99%

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Liu

et al. 2021

Sensors

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In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

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Section: Discussionmentioning

confidence: 99%

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Liu

et al. 2021

Sensors

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“…In [19], the authors presented a comparative study between the QPSO method and other methods as the PSO algorithm and FastICA algorithm, that used to improve the performance of the ICA. The comparative does under some subjective measures as the signals plotting and some objective measures as SNR and SDR measures.…”

Section: Related Workmentioning

confidence: 99%

Solve Cocktail Party Problem Based on Hybrid Method

M. Salman,

Kadhum M. Al-Qurabat,

Riyadh Finjan

2024

IJCDS

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One of the most intractable issues in contemporary digital signal processing, particularly with regards to blind source separation methods, is known as the cocktail party dilemma. This problem suppose there are many sensors record many signals at same time to produce many mixed signals. To solve this problem, one of an important methods used for this purpose is an Independent Component Analysis method. This method abbreviates in how separate mixed signals without any pre-knowledge about the mixing signals?. It treats on the statistical features of a mixing signals. This work introduces a novel method to solve the cocktail party problem, by using hybrid method from the Quantum Particle Swarm Optimization method and the Bell-Sejnowski neural method to enhance the performance of the Independent Component Analysis. In addition, the proposed method uses the Negentropy function to be the objective function of the optimization process. The proposed algorithm has been implemented on two cases of three really signals, with 8-KHz frequencies. The results of the separating process measured in two directions: firstly by comparing the results with other methods as Particle Swarm Optimization and the Quantum Particle Swarm Optimization, where the results appear that the proposed method appears very high results than other methods. Secondly, by using standard metrics as Absolute Value Correlation Coefficient, Signal to Distortion Ratio, and Signal to Noise Ratio.

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“…Where represents the -th cumulant, is an expectation operation, and is data vector of the signals [1], [7], [14], [15].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…QPSO is one of most popular meta-heuristic optimization methods based on the quantum principle of the animal nature as fishes and birds. To find the efficient solution, the meta-heuristic algorithms use the learning algorithms for an information structuring [14], [16].…”

Section: Quantum Particle Swarm Optimization (Qpso)mentioning

confidence: 99%

Bigradient neural network-based quantum particle swarm optimization for blind source separation

Salman

Al-Qurabat

Finjan

2021

IJ-AI

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An independent component analysis (ICA) is one of the solutions of a blind source separation problem. ICA is a statistical approach that depends on the statistical properties of the mixed signals. The purpose of the ICA method is to demix the mixed source signals (observation signals) and rcovering those signals. The abbreviation of the problem is that the ICA needs for optimizing by using one of the optimization approaches as swarm intelligent, neural neworks, and genetic algorithms. This paper presents a hybrid method to optimize the ICA method by using the quantum particle swarm optimization method (QPSO) to optimize the Bigradient neural network method that applies to separate mixed signals and recover sources signals. The results of an implement this work prove that this method gave good results comparing with other methods such as the Bigradient neural network and the QPSO method, based on several evaluation measures as signal-to-noise ratio, signal-to-distortion ratio, absolute value correlation coefficient, and the computation time.

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Comparative Study of QPSO and other methods in Blind Source Separation

Cited by 6 publications

References 1 publication

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Solve Cocktail Party Problem Based on Hybrid Method

Bigradient neural network-based quantum particle swarm optimization for blind source separation

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