Sonar echo signal processing based on Convolution Blind source separation

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

We investigate an acoustic echo cancellation method using a blind source separation technique. In a hands-free mobile system, when the large acoustic noise picked up by the microphone is mixed with the echo, the echo cancellation system shows poor performance. We use a blind source separation algorithm to control the acoustic noise which has to be suppressed. The blind source separation algorithm separates the original echo signal from the observation signal which is mixed with acoustic noise. The separated echo signal is applied to the reference signal of the adaptive filter; this approach gives good performance of the adaptive algorithm for echo cancellation. Computer simulation results show the efficiency of the suggested method.

This paper presented a new blind source separation algorithm using the cyclostationary statistics of source signals. On the basis of the blind source separation model, we first determined several useful assumptions. Next we get an updating equation of separation matrix, making use of the independence and cyclostationary statistics of source signals. Later we discussed the situations of real signals, complex signals and normalized algorithms. Finally, the paper compared the algorithm with other three algorithms and analyzed the convergence and performance index (PI). The simulation results demonstrated that the new presented algorithm had faster convergence speed, better separation effects and robustness, comparing to the other algorithms. Its convergence speed is nearly twice as fast as the speed of normalized EASI algorithm.

Blind Source Separation (BSS) algorithms based on the noise-free model are not applicable when the Signal Noise Ratio (SNR) is low. In view of this situation, our solution is to denoise the mixtures with additive white Gaussian noise firstly, and then use BSS algorithms. This paper proposes a piecewise Empirical Mode Decomposition (EMD) thresholding approach to denoise mixtures with strong noise. This approach can distinguish the noise-dominated IMFs and signal-dominated IMFs, and then respectively apply different thresholdings methods. Simulation results show that compared with the Wavelet denoising, the proposed approach has a better denoising performance, and can remarkably enhance the separation performance of BSS algorithms, especially when the signal SNR is low.

This work presents the application of Blind Source Separation (BSS) Algorithms on Dual-band IR Spectrogram for breast cancer detection and tracing the effect of long-term chemotherapy for breast-cancer patients. We take Dual-band IR Spectrogram's RAW Data as an input to the BSS algorithms. Integrated into the back-end processor of a Dual-band IR Sensor and Readout Circuit Platform, our Improved Neighbor-based BSS algorithm operated on Dual-band IR Spectrogram provides a better breast cancer detection. Considering the demarcating degree, our Improved Neighbor-based BSS algorithm is approximately 15% better than other algorithms. As to the correctness rate, our improved algorithm approximately increases 10% compared with other algorithms.

This letter proposes a novel method of a highly secured time domain cryptosystem to provide security for mobile voice calls. Using a modified version of blind source separation (BSS) algorithms, the proposed cryptosystem provides a complete solution for highly secured communications between the two ends of any voice call without the need for modifying the existing mobile network infrastructure. Moreover, this system can be easily connected to any type of mobile equipment. To overcome the bandwidth expansion usually associated with BSS algorithms, the proposed system uses a modified key generation process to limit the bandwidth occupied by the encrypted speech signal. Evaluation of the modified encryption and decryption algorithms proved the validity of these changes for speech signals in terms of signal to noise ratio and residual intelligibility.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation; (2) null beamforming based on the estimated DOA; (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

The availability of wireless technologies leads from monaural or bilateral hearing aids to binaural processing strategies. In this paper, we investigate a class of blind source separation (BSS)-based speech enhancement algorithms for binaural hearing aids. The blind binaural processing strategies are analyzed and evaluated for different scenarios, i.e., determined scenarios, where the number of sources does not exceed the number of available sensors and underdetermined scenarios, where there are more active source signals than microphones which is typical for hearing aid applications. These blind algorithms are an attractive alternative to beamforming as no a-priori knowledge on the sensor positions is required. Moreover, BSS algorithms have the advantage that their optimization criteria are solely based on the fundamental assumption of mutual statistical independence of the different source signals.

Blind source separation (BSS) algorithms are often categorized as either narrowband or broadband algorithms depending on whether their respective cost functions aim at individual DFT bins or the entire broadband signal. In this contribution, we present comparable general natural gradient-based formulations of both concepts based on the TRINICON framework. As a distinctive feature, narrowband algorithms imply an internal permutation and scaling problem. We show that the common DOA estimation-based methods for aligning the permutations effectively rely on geometric a-priori knowledge, and we explain why they need to be complemented by additional repair mechanisms for robust BSS. The latter can already be viewed as approximations of the generic TRINICON broadband algorithm. As a conclusion, we propose to always use a generic broadband algorithm as a starting point for the design of new BSS algorithms.

This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time- and frequency-domain implementations shows its excellent separation behavior.

Different standards to measure the sound power of a machine can be chosen. Historically, in a place with extremely loud background noise, the intensity standard (UNE-EN ISO 9614-X) has been used because there is not enough signal-to-noise relation for the pressure method (UNE-EN ISO 374x). Nevertheless, in some cases, the interfering intensity will make the measurement impossible. To solve this problem for both techniques, a new methodology based on the use of blind source separation algorithms is proposed in order to allow an estimation of the signals of the primary sources from some mixed signals and, therefore, to calculate the sound power of the machine of interest. To make the estimated signals as similar as possible to the real signals of the machine under test, different preprocessing techniques (FFT, DCT, cepstrum or wavelets) have been used before the blind source separation algorithms. Once the tests have been conducted for every machine, it is possible to establish at least one process, or combination to obtain results similar to those obtained using the international standards. Finally, since the signals can be scaled at the output of the blind source separation algorithms, one possible signal correction scheme is analyzed.

The traditional blind source separation algorithm suffers from the problems of large operation, and the convergence speed and the separation effect are constrained by each other. In this paper, the blind source separation algorithm under the natural gradient criterion is studied, and the natural gradient blind source separation algorithm is improved by combining the idea of variable step length. The method determines the separation phase of the signal by the correlation coefficient between the separated signals, and controls the step length factor according to the separation phase, and introduces the momentum term in the algorithm. Experiments prove that the improved variable step size natural gradient algorithm has the best separation performance and the fastest convergence speed compared with the adaptive step size algorithm and the fixed step size algorithm, and the separation recognition rate reaches 78.4%.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation, (2) null beamforming based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.