Single Channel Blind Source Separation Under Deep Recurrent Neural Network

He, Junfeng; Chen, Wei; Song, Yuxiao

doi:10.1007/s11277-020-07624-4

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…① The mixed music signal is pre-processed by equalization and whitening. The time-frequency signal characteristics of the mixed music signal are obtained using STFT [14] and converted into data block ) , ( t k x ( k is the frequency point and t is the time point), as shown in Figure 2. Frames are used as www.aetic.theiaer.org time points in this paper, and there are b frames. )…”

Section: Ica-based Blind Source Separation Algorithm Improved By a Co...mentioning

confidence: 99%

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Zhao¹,

Tuo²,

Guo³

et al. 2022

AETiC

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The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to the source signal. The similarity coefficient matrix, signal-to-interference ratio, performance index, and iteration time of the improved ICA-based algorithm was 62.3, 0.0011, and 0.87 s, respectively, which were all superior to the traditional ICA algorithm. The novelty of this paper is setting the initial iterative matrix of the ICA algorithm with the complex neural network.

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Section: Ica-based Blind Source Separation Algorithm Improved By a Co...mentioning

confidence: 99%

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Zhao¹,

Tuo²,

Guo³

et al. 2022

AETiC

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show abstract

“…However, in most cases,

[ 36 ] is only applicable to the measurement of the separation effect of the known simulated signals in the mixing matrix A . Therefore, we introduce the correlation coefficient as a performance index [ 37 ] to evaluate the advantages and disadvantages of the separation algorithm.

…”

Section: Simulation Analysis Of Algorithm Performancementioning

confidence: 99%

Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Liu

Wang

Zhang

2021

Sensors

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Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed. The method adds L2 regularization terms for weights and biases to the loss function to improve the steady-state performance and designs a novel optimization algorithm with a dual acceleration strategy to improve the convergence speed of the algorithm. The dual acceleration strategy of the proposed optimization algorithm smooths and speeds up the originally steep, slow gradient descent in the parameter space. Compared with competing algorithms, this strategy improves the convergence speed of the algorithm by four times and the steady-state performance index by 96%. In addition, to verify the source separation performance of the algorithm more comprehensively, the simulation data with prior knowledge and the measured data without prior knowledge are used to verify the separation performance. Both simulation results and validation results based on measured data indicate that the new algorithm not only has better convergence and steady-state performance than conventional algorithms, but it is also more suitable for engineering applications.

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“…In recent years, single-channel BSS (SCBSS), as an extreme case of BSS, has gradually become a major research topic in the eld of signal processing. SCBSS [12][13][14][15] is a technology that uses a single receiving sensor to receive and recover several source signals. This technology avoids the problems of complex system, high cost, and large equipment volume caused by multi-sensor receiving signal.…”

Section: Introductionmentioning

confidence: 99%

Improved Single-Channel Blind Source Separation Algorithm based on Variational Mode Decomposition

Zhou

Fan³

et al. 2022

Preprint

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In this paper, we propose an improved single-channel blind source separation (SCBSS) algorithm based on variational modal decomposition (VMD). VMD algorithm is a classical algorithm in SCBSS. The algorithm effectively avoids the end effect and mode mixing, and makes the decomposition more stable. However, the algorithm can not adaptively determine the number of decomposition layers. To solve this problem, we propose an improved adaptive VMD algorithm, which determines the decomposition layer of different signals by setting different spectral correlation coefficient thresholds. Combined with the improved source number estimation algorithm, signal preprocessing algorithm and JADE algorithm, SCBSS of the received mixed signal can be realized. Simulation results show that the algorithm can accurately separate source signals without bit error under different SNR and degrees of spectrum aliasing. The algorithm is effective and feasible.

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Single Channel Blind Source Separation Under Deep Recurrent Neural Network

Cited by 12 publications

References 15 publications

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

Improved Single-Channel Blind Source Separation Algorithm based on Variational Mode Decomposition

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