Single Source Point Research Articles

Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

Modal identification of non‐classically damped structures using generalized sparse component analysis

SummaryModal identification method based on blind source separation (BSS) technique has gained extensive attentions for civil structures. Developing the complex modes estimation method is important in practical applications because the assumption of proportional damping is not always satisfied. Sparse component analysis (SCA) performs well in underdetermined BSS problems. However, SCA is confined to the situation of proportional damping. In this study, a generalized SCA method is proposed to extend the original SCA method to both real and complex modes identification. First, the general formulation of complex modes is extended by the analytic form to eliminate the complex conjugate part in the BSS model. A new single‐source‐point detection method that is available to handle real and complex modes is proposed. Local outlier factor method is adopted to remove the outliers in single source points. Subsequently, complex‐valued modal matrix is calculated by the clustering technique. Then, modal responses are recovered using the complex version of smoothed zero norm method, where modal frequencies and damping ratios can be extracted. Finally, the effectiveness of the proposed method is demonstrated for identification of real and complex modes, close modes, and underdetermined problem. The application to a benchmark structure demonstrates the effectiveness for practical applications.

Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

In production practice, the signal gathered by a sensor often includes strong ambient noise, and its composition is complex. Focusing on the problem that traditional methods are difficult to separate and extract fault frequencies from strong background noise, a novel compound fault blind extraction method based on improved sparse component analysis (ISCA) and improved maximum correlation kurtosis deconvolution (IMCKD)-named ISCA‐IMCKD- is suggested. Initially, the signal that the sensor has collected is shifted into time-frequency area signal by short-time Fourier transform (STFT). In addition, the single source domain characteristic data is screened by improved single source point detection to determine the number of sources. Second, the ISCA method is optimized by using cosine distance improved fuzzy C-means clustering, which is utilized to further process the characteristic data to calculate the mixing matrix. Moreover, the estimated source signal is initially extracted according to the membership degree of clustering results. Finally, the estimated source signal is shifted into the time area by inverse STFT transform, and the IMCKD is employed to enhance the characteristics of the projected source signals. Meanwhile, the initially estimated source signal is completely separated, and the defect frequencies of the composite faults are finally extracted by envelope analysis. Simulation experiments and measured data are employed to certify the viability of the proposed means. The defect detection of rolling bearings is finished while the time cost is significantly saved.

DOA Estimation for Multiple Speech Sources Based on Flexible Single-Source Zones and Concentration Weighting

Direction of arrival (DOA) estimation is the key to many audio applications. Recently, sparse component analysis (SCA)-based methods have attracted much attention, in which single-source points (SSPs) and single-source zones (SSZs) where one source is dominant over the others in time-frequency domain are usually detected to construct the pooled histogram containing multi-source DOA information. Nonetheless, the SSZ size in existing methods is fixed and empirically predetermined, which cannot accommodate to the varying spectro-temporal property of speech sources. Furthermore, higher SSP concentration in a SSZ implies a locally stronger dominant source as well as more reliable DOA information extracted therein, which however is also not taken into account yet. To address these problems, a DOA estimation algorithm for multiple speech sources based on flexible SSZs and concentration weighting is presented in this paper. First, in each frame, correlation coefficients of time delay vectors across adjacent frequency bins are calculated to identify SSPs, followed by flexible SSZs construction using varying number of SSPs located at consecutive frequency bins. Next, the number of SSPs in each flexible SSZ is considered as a proxy of corresponding concentration degree, and employed as weighting factor to form the pooled histogram. Finally, a matching pursuit (MP)-based approach is utilized to obtain multi-source DOA estimates. Simulation results reveal that the proposed method significantly outperforms existing approaches in terms of noise floor in pooled histogram, angular resolution, and performance under various signal-to-noise ratio and reverberant conditions. Real-world experiments also verify its effectiveness, and meanwhile demonstrate considerably reduced computational complexity.

Underdetermined Blind Source Separation Method Based on a Two-Stage Single-Source Point Screening

Underdetermined blind source separation is a signal processing technique that is more suitable for practical applications and aims to separate the source signals from the mixed signals. The mixing matrix estimation is a major step in the underdetermined blind source separation. Since the current methods for estimating the mixing matrix have the disadvantages of insufficient accuracy or weak noise immunity, a two-stage single-source point screening that combines the cosine angle algorithm and the L1-norm optimization algorithm is proposed. During the first stage, the first-stage single-source points are extracted from the original mixed signals using the cosine angle algorithm. During the second stage, based on the L1-norm optimization algorithm, the reference single-source points are extracted from the original mixed signals. The reference single-source points are then clustered to obtain the clustering center, which is defined as the reference center. In combination with the reference center, the deviation and interference points in the first-stage single-source points are eliminated by the cosine distance. The remaining signal points are considered as the second-stage single-source points, which are clustered to obtain the mixing matrix estimation. Experiments on simulated and speech signals show that the proposed method can obtain more accurate and robust mixing matrix estimation, leading to better separation of the source signals.

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

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.

Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System

The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective.

Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis

In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals. In order to separate transformer acoustic signals from mixed acoustic signals obtained by a small number of sensors, a blind source separation (BSS) method of transformer acoustic signal based on sparse component analysis (SCA) is proposed in this paper. Firstly, the mixed acoustic signals are transformed from time domain to time–frequency (TF) domain, and single source points (SSPs) in the TF plane are extracted by identifying the phase angle differences of the TF points. Then, the mixing matrix is estimated by clustering SSPs with a density clustering algorithm. Finally, the transformer acoustic signal is separated from the mixed acoustic signals based on the compressed sensing theory. The results of the simulation and experiment show that the proposed method can separate the transformer acoustic signal from the mixed acoustic signals in the case of underdetermination. Compared with the existing denoising methods of the transformer acoustic signal, the denoising results of the proposed method have less error and distortion. It will provide important data support for the acoustics-based power transformer fault diagnosis.

DOA estimation of multiple speech sources based on the single-source point detection using an FOA microphone

This paper presents a method for direction of arrival (DOA) estimation of multiple speech sources based on the temporal correlation and local-frequency stationarity of speech signals. The distribution analysis of single-source points (SSPs) in a recorded signal shows that in the time–frequency (T-F) domain, the SSPs are distributed in the form of a small cluster. According to this distribution, a method for DOA estimation of multiple sound sources is developed based on the continuity between adjacent T-F points. In addition, low-reverberation single-source (LRSS) points are detected based on the phase consistency and used as guidance to detect whether adjacent T-F points are SSPs. The direction deviations between adjacent frequency points and between adjacent frames are used as the SSP detection criteria considering the temporal correlation and local-frequency stationarity. The kernel density estimation and peak search are performed to obtain the dynamic DOA estimation range of each source. Finally, DOA estimates of each source are obtained by statistical weighting-based fine localization. Experiments under both simulated and real conditions show that the proposed method can achieve better localization performance than several existing methods.

Underdetermined mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition

Underdetermined mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition

Underdetermined blind source separation of radar signals based on genetic annealing algorithm

Aiming at the problem that the traditional underdetermined blind source separation (UBSS) algorithm does not have high accuracy in estimating the mixing matrix and the number of source signals cannot be determined, a genetic annealing UBSS method with source number estimation function is proposed. This method first performs a short-time Fourier transform on the observed signal, and then performs single-source point detection and normalization processing to transform linear clusters into dense clusters. In view of the strong global search ability of the genetic algorithm (GA) and the good local optimization ability of the simulated annealing algorithm, the GA and simulated annealing algorithm are combined, and the number of source signals is determined according to the BWP (Between-Within Proportion, BWP) index. After estimating the mixing matrix, a modified subspace projection method is used to reconstruct the source signal. The simulation results of four kinds of radar signals show that in the case of dual receiving channels and three receiving channels, compared with the traditional method, the similarity coefficients of the proposed algorithm are greater, which can effectively improve the mixing matrix estimation accuracy, and can accurately determine the number of source signals.

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

Unmixing methods based on nonnegativity and weakly mixed pixels for astronomical hyperspectral datasets

An increasing number of astronomical instruments (on Earth and space-based) provide hyperspectral images, that is three-dimensional data cubes with two spatial dimensions and one spectral dimension. The intrinsic limitation in spatial resolution of these instruments implies that the spectra associated with pixels of such images are most often mixtures of the spectra of the “pure” components that exist in the considered region. In order to estimate the spectra and spatial abundances of these pure components, we here propose an original blind signal separation (BSS), that is to say an unsupervised unmixing method. Our approach is based on extensions and combinations of linear BSS methods that belong to two major classes of methods, namely nonnegative matrix factorization (NMF) and sparse component analysis (SCA). The former performs the decomposition of hyperspectral images, as a set of pure spectra and abundance maps, by using nonnegativity constraints, but the estimated solution is not unique: It highly depends on the initialization of the algorithm. The considered SCA methods are based on the assumption of the existence of points or tiny spatial zones where only one source is active (i.e., one pure component is present). These points or zones are then used to estimate the mixture and perform the decomposition. In real conditions, the assumption of perfect single-source points or zones is not always realistic. In such conditions, SCA yields approximate versions of the unknown sources and mixing coefficients. We propose to use part of these preliminary estimates from the SCA to initialize several runs of the NMF in order to refine these estimates and further constrain the convergence of the NMF algorithm. The proposed methods also estimate the number of pure components involved in the data and they provide error bars associated with the obtained solution. Detailed tests with synthetic data show that the decomposition achieved with such hybrid methods is nearly unique and provides good performance, illustrating the potential of applications to real data.

Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

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. The received signals are first processed using an adaptive denoising method. They are then divided into different appropriate length frames. Next, the frame type is determined and each frame is processed in accordance with its type. Short-time Fourier transform is used to transform the mixed signals from time domain to time–frequency domain. A transformation matrix is introduced to resolve the issue of the traditional clustering algorithm not being applicable within the complex field. After introducing the transformation matrix, an innovative single-source point detection algorithm and an estimation algorithm for the number of source signals are proposed. Finally, the mixing matrix is estimated via a fuzzy c-means clustering algorithm based on the characteristics of complex numbers. The simulation results show that the proposed algorithm improves the estimation accuracy of the mixing matrix considerably. Further, it has evident advantages for blind estimation of the mixing matrix for time-varying radar signals in the dynamic system.

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transform matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.

Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points

In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both “points” (single T-F points) and “regions” (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Multi-source DOA estimation in reverberant environments using potential single-source points enhancement

Direction-of-arrival (DOA) estimation of multiple speech sources in enclosed environments is a hot topic in the acoustic field. Reverberation and multi-source overlapping are the primary challenges in multi-source localization. These disturbances result a performance degradation. In this paper, a new method for multi-source DOA estimation in reverberant environments is presented using potential single-source points enhancement. First, we made a statistical investigation about the effect of reverberation on single-source points (SSPs) whose components dominantly derive from only one source. From the experimental results, it is found that several time-frequency points detected as SSPs in anechoic environments are converted into non-SSPs when the signals are recorded in reverberant conditions. These points are defined as potential SSPs in this paper. Then, potential SSPs enhancement algorithm is designed to suppress the effect of reverberation for SSPs detection. Finally, DOA estimation is accomplished by post-processing the histogram, which is generated by the DOA estimates calculated at the detected SSPs. We conducted objective evaluations in both simulated and real environments. Experiment results indicate that the localization scheme proposed in this paper is more efficient with some state-of-the-art localization methods in terms of both DOA estimation and source counting.

Underdetermined convolutive blind source separation in the time–frequency domain based on single source points and experimental validation

To effectively separate underdetermined convolutive mixtures, an accurate and robust underdetermined convolutive blind source separation (BSS) method in the time–frequency domain based on single source points (SSPs) is proposed. Firstly, convolutive BSS in the time domain is transformed into instantaneous BSS in the time–frequency domain by short-time Fourier transform (STFT). Secondly, hierarchical clustering is carried out after the extraction and identification of SSPs by using sparse coding technology. Thirdly, source separation in the frequency domain is obtained based on Lp norm minimization and then alignments are permutated and scales are compensated. Finally, inverse STFT is adopted to reconstruct the separated signals in the time domain. Furthermore, numerical case studies and experimental case studies on a cylindrical test bed are carried out to validate the effectiveness of the proposed method. Generally, this paper provides an accurate and robust underdetermined convolutive BSS technique for mixing matrix estimation, source identification and contribution evaluation, which can provide significant evidence for vibration and noise monitoring and control.

A novel mixing matrix estimation algorithm in instantaneous underdetermined blind source separation

Due to the lack of sufficient prior information, how to estimate the mixing matrix in multiple underdetermined blind source separation (UBSS) models is a difficult problem. This study proposes an algorithm, which is used for the estimation of mixing matrix in instantaneous UBSS. Firstly, we propose an efficient single-source-points detection criterion with the transformation for the mixed signal vector, which is used as the basis for the clustering process. There are some shortcomings in the classical clustering algorithms, including the dependence on input parameters, restrictions on the data dimension, the requirement of the prior knowledge of the source signals and high complexity. To overcome these drawbacks, the modified density peaks clustering algorithm is used for the estimation of the initial clustering centers to adapt to different circumstances. Based on the idea of mean clustering, the single source points near each initial cluster center are processed, respectively, and the final estimation results of the mixing matrix are obtained. A variety of simulation experiments demonstrate the universality and validity of the proposed algorithm. The proposed method also has excellent performance even under the circumstance of low signal-to-noise ratio.

Underdetermined mixing matrix estimation by exploiting sparsity of sources

To estimate the mixing matrix in underdetermined mixing systems, we propose a novel method by exploiting the sparsity of sources. We utilize the pairwise relationships among all of the mixture representations to detect the single source points in the time-frequency (TF) domain, i.e., the positions where only one source contributed dominantly. The mixture representations at these single source points are then clustered to estimate the underlying mixing matrix. Since the pairwise relationships among all mixtures are considered in the TF domain, the proposed method can achieve an accurate mixing matrix estimation and be robust in noisy cases. Experimental results indicate that our method is effective in mixing matrix estimation and outperforms five peer methods.