A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Lu, Jiming; Cheng, Wei; Ye, Zi

doi:10.3390/s19061413

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…To estimate the mixing matrix, it employed a fixed parameter to decide which STFT coefficients to use before looking for the SSPs. Only a few TF coefficients were found to be sufficient to fully estimate the mixing matrix before identifying single-source locations, as stated by [ 8 ]. However, there is another possible method of improving the accuracy and computing the efficiency, which is by using a TF selection factor.…”

Section: Mixing Matrix Estimationmentioning

confidence: 99%

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Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Hassan

Ramli

2023

Sensors

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Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estimation. Adaptive time–frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen’s method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.

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Section: Mixing Matrix Estimationmentioning

confidence: 99%

“…UBSS has garnered substantial scholarly interest, and numerous solutions to this issue have been offered [ 6 , 7 , 8 , 9 , 10 ]. UBSS is classified into two classes, namely, statistic characteristic and sparsity [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Hassan

Ramli

2023

Sensors

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“…If ∆θ is too large, the condition of equation ( 10) becomes loose and many outliers will be misjudged as SSPs, which will reduce the accuracy of the mixing matrix estimation. Furthermore, as described in [36], since ∆θ is related to the property of source signals, it is hard to give a unified standard for all kinds of signals. Therefore, a feasible approach is to set a smaller threshold with a priori knowledge and watch the scatter plot in the TF domain.…”

Section: Optimized Sca-based Underdetermined Blind Modal Identificationmentioning

confidence: 99%

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Guan

Dong

Yin-shan

et al. 2019

Meas. Sci. Technol.

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Modal identification performs one of the most important roles in structural dynamics analysis and structural health monitoring, especially when the input excitations are not measurable. Most of the traditional blind source separation approaches can only handle determined or overdetermined blind modal identification, where the number of observed sensors is equal to or greater than the number of active modes. When the number of observed sensors is less than the number of active modes, new methods to perform underdetermined blind modal identification should be considered. To tackle this issue, a novel operational modal identification method based on an enhanced sparse component analysis with optimized clustering is proposed. Firstly, a robust K-means clustering with differential evolution algorithm is put forward to estimate the mode shape matrix utilizing the sparse property of observed mixtures. Secondly, the modal responses are recovered by the least squares method from the incomplete knowledge of the mode shape matrix and the system outputs. Subsequently, the modal responses are transformed into a time domain through time-frequency transformation, where the modal parameters are extracted. Finally, numerical simulation and experimental verification demonstrate that in both the determined and underdetermined case, the proposed method can perform accurate and robust parameter identification of structural systems.

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“…Under the assumption of sparse signals, the estimation of the mixing matrix can be transformed into a clustering problem that is solved by a clustering algorithm. Traditional clustering algorithms, such as Fuzzy C-Means (FCM), require prior knowledge of the number of sources, making it less suitable for underdetermined scenarios [ 31 , 32 ]. To address this limitation, the DBSCAN method has been introduced to estimate the number of clustering centers, thereby overcoming the dependency on pre-determined source counts.…”

Section: Introductionmentioning

confidence: 99%

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors

Li,

Wang,

Zhao

et al. 2023

Sensors

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Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

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A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Cited by 11 publications

References 22 publications

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors

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