Blind Separation Algorithm Research Articles

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

NNMF with Speaker Clustering in a Uniform Filter-Bank for Blind Speech Separation

This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm’s input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB.

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.

Single-Channel Blind Image Separation Based on Transformer-Guided GAN

Blind source separation (BSS) has been a great challenge in the field of signal processing due to the unknown distribution of the source signal and the mixing matrix. Traditional methods based on statistics and information theory use prior information such as source distribution independence, non-Gaussianity, sparsity, etc. to solve this problem. Generative adversarial networks (GANs) learn source distributions through games without being constrained by statistical properties. However, the current blind image separation methods based on GANs ignores the reconstruction of the structure and details of the separated image, resulting in residual interference source information in the generated results. This paper proposes a Transformer-guided GAN guided by an attention mechanism. Through the adversarial training of the generator and the discriminator, U-shaped Network (UNet) is used to fuse the convolutional layer features to reconstruct the structure of the separated image, and Transformer is used to calculate the position attention and guide the detailed information. We validate our method with quantitative experiments, showing that it outperforms previous blind image separation algorithms in terms of PSNR and SSIM.

Leak Location for Urban Elbowed Water Pipe Based on Complex-Optimized FastICA Blind Deconvolution

Previous research has primarily focused on leak diagnosis algorithms under the interference of outside environment noise. However, the former algorithms in leak detection are easily affected by noise from the pipe, especially the one that contains elbow noise. This article was mainly concerned with the effective and reliable approach to precisely locate leaks in the buried elbow pipeline system. To overcome the drawback of elbow noise interference and extract the leak source from the mixing signal in the greatest extent, this article proposes a novel frequency-domain-independent component analysis (ICA) blind deconvolution algorithm, which is based on a popular optimized FastICA (O-FICA) called complex-optimized FastICA (CO-FICA) algorithm. In comparison, CO-FICA differs from the time-domain blind convolution separation (T-BCS) algorithm, which transforms complicated time-domain convolution into a simple frequency-domain product problem. Compared with the conventional complex FastICA (C-FICA), the proposed CO-FICA method does not need to estimate signal statistics at each step, but it displays more excellent performance in convergent speed and separated precision. Altogether, the results from this study demonstrate that the localization precision of the proposed method is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 87\%$ </tex-math></inline-formula> under the interference of elbow noise.

Анализ эффективности использования методов слепого разделения сигналов для борьбы с помеховым излучением электрических ракетных двигателей в системах космической связи

The article considers the use of blind signal separation algorithms to improve interference immunity of the satellite communication channel with octal phase shift keying as applied to the interference, that is caused by the operation of stationary plasma thrusters (SPT), and represents one of the varieties of electric propulsion [1]. During the study, a simulation model of communication channel with an additional channel for receiving interference from SPT was developed. While modeling, some pulsed noise-like signals, formed as a result of SPT operation, were considered as interference in the communication channel. The blind signal separation algorithm ncFastICA used in the study is based on the method of independent component analysis. The main task of the modeling was to verify the applicability of blind signal separation methods to counter the interference from SPT in the communication channel. The simulation results proved the possibility of efficient use of blind signal separation algorithms to counter SPT interference. Application of the considered algorithm allows achieving a gain in bit error rate by two and more orders of magnitude. Thus, one of the worst cases of signal reception was considered: the gain from the use of the ncFastICA algorithm is achieved when the bit signal-to- noise ratio is more than 10 dB and when the interference-to-signal ratio in the signal-receiving channel is 10 dB and in the interference receiving channel from the SPT is 30 dB.

A MLE-based blind signal separation method for time–frequency overlapped signal using neural network

The blind signal separation (BSS) algorithm obtains each original/source signal from the observed signal collected by the receiving antenna or sensor. Objective/loss/cost function and optimization method are two key parts of BSS algorithm. Modifying the objective function and optimization from the perspective of neural network (NN) is a novel concept in BSS domain. L_2 regularization is adopted as a term of maximum likelihood estimation (MLE)-based objective function like in Liu et al. (Sensors 21(3):973, 2021); however, we modified the probability density function (PDF) term of the objective function and used the kernel density estimation method for time–frequency overlapped digital communication signal. Multiple optimizers are studied in this paper, and we figure out the right optimizer for our application scenario. A varies of comparison experiments—whose separation results will be provided in forms of correlation coefficient and performance index—are carried out, which indicate our method can converge quickly and achieve satisfactory separation results with performance index (PI) lower than 0.02 when signal-to-noise ratio (SNR) no less than 10dB. Additionally, it demonstrates performance of our method is better than that of typical separation—FastICA, especially for the lower SNR environment, and it shows that our method is not sensitive to the frequency overlap level (FOL) of the source signal, even FOL as high as 100%; it still can get high-precision separation results with textrm{PI}<0.02.

Accelerated Conjugate Gradient for Second-Order Blind Signal Separation

This paper proposes a new adaptive algorithm for the second-order blind signal separation (BSS) problem with convolutive mixtures by utilising a combination of an accelerated gradient and a conjugate gradient method. For each iteration of the adaptive algorithm, the search point and the search direction are obtained based on the current and the previous iterations. The algorithm efficiently calculates the step size for the accelerated conjugate gradient algorithm in each iteration. Simulation results show that the proposed accelerated conjugate gradient algorithm with optimal step size converges faster than the accelerated descent algorithm and the steepest descent algorithm with optimal step size while having lower computational complexity. In particular, the number of iterations required for convergence of the accelerated conjugate gradient algorithm is significantly lower than the accelerated descent algorithm and the steepest descent algorithm. In addition, the proposed system achieves improvement in terms of the signal to interference ratio and signal to noise ratio for the dominant speech outputs.

FPGA based Hardware Co-Simulation Implementation for RealTime Image Blind Separation using ICA Algorithms

The use of mixed image sources requires digital preprocessing to separate them, for making easier their storage and improve their quality. The ICALAB for Image Processing method is one of the solutions proposed for the blind efficient separation of signals without prior knowledge of the source images. Nevertheless, to choose the appropriate algorithm with the most efficient separation index, several tests are carried out on a certain number of algorithms, with off-line processing of image samples bases, by means of software tools which are generally installed on a computer, such as ICALAB, TIPICA, GIFT, NMFLAB, model identification UBSS, ... Thus, in order to perform the image separation processing in real time, this study aims to develop a rapid prototyping embedded system for implementing the blind source separation concept. It mainly provides a promising approach for implementing twelve efficient blind image separation algorithms on the ZYBO Z7 FPGA board, using Xilinx System Generator (XSG) for Real Time Hardware Co-simulation. As an application example, this study deals with the blind separation of two e-frames mixture, and then provides the adequate algorithm, basing on the calculation of the Performance Index (PI), and the separated images results. Keywords—Blind Image Separation; Hardware CoSimulation; Independent Component Algorithm (ICA); Xilinx System Generator (XSG); Zynq-7000; VHDL.

An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number

It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms.

Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.

Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

Adaptive step size EASI algorithm based on nonlinear correlation for on-line separation of satellite micro-vibration sources

The on-line blind separation of satellite micro-vibration source signals can provide the basis for the on-line suppression of satellite micro-vibration from the perspective of vibration source control, which has important academic significance and remarkable engineering application value for improving the performance of satellite positioning accuracy. Focusing on the inherent contradiction between convergence rate and steady-state error in on-line separation, a novel adaptive step size EASI (Equivariant Adaptive Separation via Independence) algorithm is proposed. Since nonlinear uncorrelation of Blind Source Separation (BSS) results displays its independence equivalently, minimizing nonlinear correlation also marks the optimal state of separation. According to this basic BSS estimation principle, a separation index based on nonlinear correlation coefficients is constructed to represent the separation degree, and on-line updating equations of separation index is derived by time average technique. Then, a suitable nonlinear monotone function is adopted to map separation index to the step size, and the step size can be adaptively adjusted according to separation degree. Finally, the novel adaptive blind separation algorithm in this paper is formed by introducing the adaptive step size to traditional EASI algorithm. Compared with fixed step size EASI algorithm (FS-EASI) and several variable step size EASI algorithms, the proposed algorithm greatly improves convergence rate while ensuring high separation accuracy. Effectiveness and superiority of the proposed algorithm are verified by simulation case analysis and satellite cabin structure experimental study.

Convolution separation and application of joint diagonalization with optimal parameters on mechanical signals

Blind separation algorithm has the problem of separation stability in the condition of multiple convolution signals. Based on cluster analysis, combined with the optimal distance matrix and the optimal window, the joint diagonalization is improved, which can effectively improve the stability and accuracy of the signal separation. The full frequency divergence is set as the objective function of permutation ambiguity in the convolution separating process, the failure from convoluted blind source separation is solved. Combining with new joint diagonalization and convolution separation is to form a system approach, and is applied to the floor signal from the actual bench, the influence of excitation source in frequency is achieved, the system algorithm can be used as a reference of mechanical vibration analysis.

A Comparison of Block Based Kalman Filter and H-Infinity Algorithms for Blind Image Separation

A Comparison of Block Based Kalman Filter and H-Infinity Algorithms for Blind Image Separation

FMRI activations via low-complexity second-order inverse-sparse-transform blind separation

Since functional Magnetic Resonance Imaging (fMRI) signals are a group of sparse signals, and its autocorrelation matrix contains limited information, it is difficult to accurately locate the brain activation area directly using traditional blind separation algorithms. For the issue, this paper proposes a method with inverse-sparse transform and second order blind identification (SOBI) for the separation of the activations. The contribution of this paper is to achieve the separation of sparse brain map signals and have lower computational complexity than higher-order statistical BSS. In experiments, we use both simulated and measured fMRI data to evaluate our method. The experimental results show that the proposed method's running time is only 1/30 of a higher-order statistical independent component analysis (ICA) algorithm, while its separation errors is close to ICA and less than half of a traditional SOBI algorithm.

2D DOA Estimation of Coherently Distributed Sources for Planar Arrays Based on Blind Signal Separation

A novel method is presented to estimate the direction-of-arrival (DOA) of coherently distributed (CD) sources for planar arrays. In the proposed method, the two-dimensional (2D) angular parameters are obtained by making use of the estimated generalized array manifold matrix, which is separated from the received observation data based on blind signal separation algorithm. The proposed method offers a number of advantages. First, the 2D central DOAs and angular spread variances can be estimated simultaneously. Second, the deterministic angular distributed functions (DADFs) of the CD sources do not need to know. Third, the special array geometries are not required, as the method is applicable to planar arrays. Computer simulations are shown to verify the efficacy of the proposed method.

Dynamic welding process monitoring based on microphone array technology

Abstract In this paper, microphone array technology was used to monitor the dynamic pulsed GMAW process. At first, the splash sound signal is successfully separated out based on FastICA blind signal separation algorithm, and its frequency domain energy distribution is mainly concentrated in the high frequency band of 6000−8000 Hz. Through time and frequency domain analysis, it is found that the short-time energy of 500−1000 Hz band of observed signal and the short-time energy of splash signal can identify the burn-through defects well, furthermore the ratio of them can be used as a robust feature because of its high sensitivity and anti-interference performance. Since the splash sound signal is a characteristic signal separated by microphone array, it is shown that the abundant dynamic information provided by microphone array can better assist in the identification and monitoring of welding defects compared to a single microphone. In order to solve the serious imbalance problem between positive and negative samples, the logistic regression and BP neural network model are improved based on the machine learning method. The experimental results show that the recognition accuracy of the optimization models have been greatly improved, even could reach 99.6 %.

Novel implementation of “Filtered-Reconstruction ICA” enabled wheeze component separation for preliminary diagnosis of COVID-19

The outbreak of COVID-19 pandemic has made the whole world at a standstill. With researches advancing towards invention of vaccines, in this project work, we present a faithful and novel approach towards preliminary diagnosis of COVID-19 for infants in the age group of 2-24 months. Wheeze is a prime symptom of COVID-19 infection. Due to cry of babies, many a times the intensity of wheeze cannot be evaluated, which leads to improper medications or even mortality. For this, the concept of Blind Source Separation using Independent Component Analysis (ICA) is utilized. Reconstruction Independent Component Analysis (RICA) is one of the primarily utilized ICA technologies. This technique is a conventionally used mechanism in order to estimate airplanes and also equally utilized in the biomedical field for extracting out desired signals from the interference mixtures like electrocardiogram (ECG), magnetoencephalography (MEG) and electroencephalogram (EEG). Henceforth, RICA is accomplished for the real time processing of signals. In this paper, an algorithm based on RICA and a Least Mean Square (LMS) filter has been developed. The idea behind this paper is to extract the wheeze intensity from a mix of cry and wheeze of infants infected with the disease. The aim of this work is to implement an effective blind component separation algorithm in which a RICA method is used to extract the wheeze components and further an LMS filter is used to enhance the SNR, so that even a remote doctor could prescribe appropriate medication based on the wheeze intensity.

Blind separation of noncooperative paired carrier multiple access signals based on improved quantum‐inspired evolutionary algorithm and receding horizon optimization

AbstractThe single‐channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high‐order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel estimation and sequence detection, and a novel blind separation algorithm based on the improved quantum‐inspired evolutionary algorithm (IQEA) and receding horizon optimization was proposed. Considering the practical scenarios, a mixed PCMA signal model with noninteger period sampling was presented in advance. Combined with the proposed signal model, the IQEA was applied to recover the uplink signals from the mixed PCMA signals by its iterative estimation. Meanwhile, to reduce the computational complexity caused by long memory of PCMA signals, the receding horizon optimization was introduced to process the whole signal sequence as a dynamic programming problem. The simulation results verified that the proposed RH‐IQEA outperforms existing state‐of‐the‐art separation algorithms in the blind separation of noncooperative PCMA signals, with higher accuracy, lower‐order complexity and stronger robustness to parameter estimation errors.