Introduction to Underdetermined Blind

What is/are Underdetermined Blind?

Underdetermined Blind - In order to solve the problem of matrix estimation in underdetermined blind source separation, a matrix estimation method based on single source labeling was proposed. ^[1] In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. ^[2] The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. ^[3] Nevertheless, it is seen that real implementation is very rarely done in existing researches because the real-time Implementation of UBSS (Underdetermined Blind Source Separation) exists to be a challenging one due to its lacking hardware characteristics of increased latency, reduced speed and consumption of more memory space. ^[4] In our experiment, we apply our method to a variety of real-world problems, including image denoising, image deraining, image shadow removal, non-uniform illumination correction, and underdetermined blind source separation of images or speech signals. ^[5] Further, for the underdetermined problem, that is, the number of microphones is less than the number of source signals, the improved joint diagonalization algorithm and the time-frequency (T-F) mask are combined to realize the underdetermined blind source separation based on the double microphones, and a variety of algorithms are compared with it for performance evaluation. ^[6] In order to solve these problems, an underdetermined blind source separation (UBSS) approach with source number estimation and improved sparse component analysis (SCA) is studied. ^[7] Since the number of observed signals is less than the number of source signals, underdetermined blind source separation (UBSS) is a kind of ill-posed blind source separation (BSS) problem. ^[8] Underdetermined blind source separation of speech mixtures is a challenging issue in the classical “Cocktail-party” problem. ^[9] In underdetermined blind source separation (UBSS), the estimation of the mixing matrix is crucial because it directly affects the performance of UBSS. ^[10]

mixing matrix estimation

This paper presents a novel mixing matrix estimation method based on a frame cluster analysis for application to the dynamic system of radar signals under an underdetermined blind source separation. ^[1] In this paper, a single source points (SSPs) detection method based on local dominance is devised for mixing matrix estimation in underdetermined blind source separation (UBSS). ^[2] The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. ^[3]

underdetermined blind source

In order to solve the problem of matrix estimation in underdetermined blind source separation, a matrix estimation method based on single source labeling was proposed. ^[1] In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. ^[2] The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. ^[3] Nevertheless, it is seen that real implementation is very rarely done in existing researches because the real-time Implementation of UBSS (Underdetermined Blind Source Separation) exists to be a challenging one due to its lacking hardware characteristics of increased latency, reduced speed and consumption of more memory space. ^[4] In our experiment, we apply our method to a variety of real-world problems, including image denoising, image deraining, image shadow removal, non-uniform illumination correction, and underdetermined blind source separation of images or speech signals. ^[5] Further, for the underdetermined problem, that is, the number of microphones is less than the number of source signals, the improved joint diagonalization algorithm and the time-frequency (T-F) mask are combined to realize the underdetermined blind source separation based on the double microphones, and a variety of algorithms are compared with it for performance evaluation. ^[6] In order to solve these problems, an underdetermined blind source separation (UBSS) approach with source number estimation and improved sparse component analysis (SCA) is studied. ^[7] Since the number of observed signals is less than the number of source signals, underdetermined blind source separation (UBSS) is a kind of ill-posed blind source separation (BSS) problem. ^[8] This paper presents a novel mixing matrix estimation method based on a frame cluster analysis for application to the dynamic system of radar signals under an underdetermined blind source separation. ^[9] Hence, the fault characterization is actually a nonlinear underdetermined blind source separation (NUBSS) problem. ^[10] In this paper, a single source points (SSPs) detection method based on local dominance is devised for mixing matrix estimation in underdetermined blind source separation (UBSS). ^[11] The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. ^[12] Underdetermined blind source separation of speech mixtures is a challenging issue in the classical “Cocktail-party” problem. ^[13] In underdetermined blind source separation (UBSS), the estimation of the mixing matrix is crucial because it directly affects the performance of UBSS. ^[14] In practical application, the collected measurements are always a mixture of signals from many unknown sources, which is considered challenging as an underdetermined blind source separation (UBSS) problem for fault location and recognition. ^[15] Since roller bearing is one of the most vulnerable components, bearing faults usually occur in an unprepared situation with multiple faults, and the quantity of sensors is limited in the real-time working environment, resulting in an underdetermined blind source separation (UBSS) problem to extract the fault features. ^[16] In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. ^[17] To address this problem, a novel underdetermined blind source separation (UBSS) method is proposed using synchrosqueezing transform (SST) and improved density peaks clustering (DPC). ^[18] Considering the time-delayed mixing model of the underdetermined blind source separation problem, we propose a novel mixing matrix estimation algorithm in this paper. ^[19] To improve the mixing matrix estimation performance of frequency hopping (FH) signals under the underdetermined blind source separation (UBSS) model, a new estimation method is proposed in this paper. ^[20] Moreover, due to the practical limitations, the number of sensors is usually less than that of the source signals, which makes it an underdetermined blind source separation (BSS) problem to identify the fault signals. ^[21] Shortest path algorithm is a simple algorithm used for source recovery of underdetermined blind source separation (UBSS), but this algorithm is only applicable to the case where the number of sensors is two. ^[22] Aiming at the problem that the signal recovery accuracy of the underdetermined blind source separation algorithm is low, the mixed matrix estimation algorithm is improved by using the sparse characteristics of the signal time-frequency domain. ^[23] To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. ^[24] The authors propose a discrete wavelet transform-based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using a novel parameter, i. ^[25] Underdetermined blind source separation is to recover the source signals from the observed signals without prior knowledge of the mixing channel. ^[26] Based on this, Variational Mode Decomposition algorithm is applied to the problem of underdetermined blind source separation. ^[27] Underdetermined blind source separation (UBSS) is a hot and challenging problem in signal processing. ^[28] Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. ^[29] Mixing matrix estimation is one of the key techniques in underdetermined blind source separation. ^[30] This paper proposed an improved sparse component analysis (SCA) approach to improve the performance of underdetermined blind source separation for the acoustic/speech sources. ^[31]

underdetermined blind speech

The existing methods for source counting in underdetermined blind speech separation suffer from the overlapping between sources with low W-disjoint orthogonality. ^[1] In this research a new way introduced for solving the underdetermined blind speech signal separation problem when the number of observation is less than the sources for which the ICA is no longer applicable, which enhance the time complexity for separation of signal. ^[2]