A low-complexity permutation alignment method for frequency-domain blind source separation

Kang, Fang; Yang, Feiran; Yang, Jun

doi:10.1016/j.specom.2019.11.002

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…The number of voice signals is N = 4, the length of the voice signals is 10 s, and the sampling frequency is f s = 8000Hz. As shown in TABLE 5, JADE runtime is 3.4 s. Meanwhile, the running time of the algorithm proposed in literature [25] and this paper is 10.3 s and 9.6 s, respectively. Compared with JADE, the calculation of the permutation algorithm cannot be disregarded.…”

Section: B Algorithm Complexity Analysismentioning

confidence: 84%

“…Compared with JADE, the calculation of the permutation algorithm cannot be disregarded. Compared with reference [25], this algorithm has a shorter running time. Based on this analysis of the computational complexity of this algorithm, while ensuring accurate separation of the source voice signal, the computational complexity is reduced.…”

Section: B Algorithm Complexity Analysismentioning

confidence: 97%

“…The number of multiplications required by the algorithm is To compare the computational complexity of various algorithms, the running time of the frequency-domain permutation algorithm in reference [25] is given. Select the same operating environment.…”

Section: B Algorithm Complexity Analysismentioning

confidence: 99%

“…These algorithms all have a good effect in solving BSS in the frequency domain, but they also have a high computational complexity. Then K. Fang and others presented a low-complexity permutation alignment method based on the inter-frequency dependence of signal power ratio to resolve permutation problem [25]. This algorithm can reduce its computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

Guo

et al. 2020

IEEE Access

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Since voice communication is the main method of information transmission, it is important to ensure the safety and efficiency of voice communication. In this paper, a more complex multipath channel model in a wireless environment is considered, and chaotic masking technology is used to ensure the security of voice signal transmission. Based on this, a multipath blind source separation algorithm based on cross-correlation permutation of spectral peak search counters is proposed. First, chaotic masking is performed between multiple voice signals and chaotic signal, and an impulse response filter is used to simulate the multipath transmission process. Next, the short-time Fourier transform is employed to convert the observation signal into a frequency-domain signal. Then, the joint approximate diagonalization of eigenmatrix algorithm is used to perform linear instantaneous blind source separation at each frequency. And to solve the permutation uncertainty problem in the frequency domain method, a cross-correlation permutation algorithm based on a spectral peak search counter is proposed. Finally, the permuted signal is transformed back to the time domain to get the estimated source signal by the inverse short-time Fourier transform. And simulation experiments show that the algorithm greatly reduces the calculation amount of the autocorrelation coefficient in the traditional permutation algorithm. Moreover, the permutation algorithm has stability and accuracy, and provides a solution for the secure transmission and effective separation of voice signals in multipath transmission channels. INDEX TERMS Chaotic masking, convolutive mixture blind source separation, cross-correlation permutation, short-time Fourier transform.

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Section: B Algorithm Complexity Analysismentioning

confidence: 84%

Section: B Algorithm Complexity Analysismentioning

confidence: 97%

Section: B Algorithm Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

Guo

et al. 2020

IEEE Access

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show abstract

“…Let the number of frequency points of STFT be K and the total number of frames of data be B. Assume that the number of source signals N is the same as the number of observed signals M. For convenience, only multiplication operations are considered when calculating the complexity, and the complex-valued multiplication is a four-fold relation of the real-valued multiplication [43]. The real-valued multiplication operations required for the main procedure are shown in Table 5, where N p represents the number of iterations of the Fast IVA algorithm.…”

Section: Complexity Analysismentioning

confidence: 99%

An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking

et al. 2021

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As the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the convolutional signal are crucial steps in obtaining source information. In this paper, chaotic masking technology is used to guarantee the transmission safety of speech signals, and a fast fixed-point independent vector analysis algorithm is used to solve the problem of convolutional blind source separation. First, the chaotic masking is performed before the speech signal is sent, and the convolutional mixing process of multiple signals is simulated by impulse response filter. Then, the observed signal is transformed to the frequency domain by short-time Fourier transform, and instantaneous blind source separation is performed using a fast fixed-point independent vector analysis algorithm. The algorithm can preserve the high-order statistical correlation between frequencies to solve the permutation ambiguity problem in independent component analysis. Simulation experiments show that this algorithm can efficiently complete the blind extraction of convolutional signals, and the quality of recovered speech signals is better. It provides a solution for the secure transmission and effective separation of speech signals in multipath transmission channels.

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Low-latency real-time independent vector analysis using convolutive transfer function

Wang

Yang

et al. 2022

Applied Acoustics

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A low-complexity permutation alignment method for frequency-domain blind source separation

Cited by 9 publications

References 17 publications

Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking

Low-latency real-time independent vector analysis using convolutive transfer function

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