Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization

Xie, Yuan; Xie, Kan; Yang, Junjie; Xie, Shengli

doi:10.3390/sym10100521

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Blind source separation (BSS) refers to the problem of separating source signals from the mixed observation signal when the signal mixing process is unknown [1][2][3]. In recent years, it has become a research focus, which is widely used in speech signal processing [4,5], biomedical signal processing [6,7], radar communication signal processing [8][9][10], fault diagnosis [11,12], and other fields. The number of observation sensors is often taken for granted in real applications, but the number of source signals is unknown and often exceeds the number of observation signals [13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…However, in practice, it is unknown and has an important effect on the channel identification. Especially, in the underdetermined convolutive mixture model [6][7][8], it is a difficult issue due to the complexity of the model. Therefore, the effective channel order determination is a challenging problem to be solved in the blind channel identification.…”

Section: Introductionmentioning

confidence: 99%

Effective Channel Order Determination Algorithm for Convolutive Blind Channel Identification

Yang¹,

Su²,

Li³

et al. 2019

IJCIS

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Effective channel order determination is an important problem in convolutive blind channel identification. The classical techniques are based on information theoretic criteria, which show a great potentiality to estimate the effective channel order. However, these methods are just effective for the overdetermined case, i.e., the number of sensors is larger than the number of source signals. When the number of sensors is less than or equal to the number of source signals, i.e., in the underdetermined or determined case, it is difficult to detect the effective channel order. In this paper, an improved algorithm is proposed to estimate the effective channel order by integrating numerical analysis arguments and higher-order cumulant tensor. In the proposed algorithm, we exploit the information contained in the higher-order data statistics and rearrange the tensor as a matrix using the unfolding operation, then utilize the eigenvalues of the matrix and combine numerical analysis arguments to detect the effective channel order. Finally, a series of experiment results demonstrate the effectiveness and superiority of the proposed algorithm.

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“…Because the estimated mixing matrix is not invertible, so that perfect separation is not possible. Various BSS algorithms have been proposed in the past [10,11], where the source separation is performed in the time-frequency domain via short-time Fourier transform (STFT). In the frequency domain, the observed mixtures can be represented as the product between source signals and complexvalued mixing vectors.…”

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confidence: 99%

Multi‐channel underdetermined blind source separation for recorded audio mixture signals using an unmanned aerial vehicle

Xie

Jiang²,

Yang

2021

IET Communications

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Unmanned aerial vehicles as an important role for 5G and beyond networks are becoming more and more popular and have been equipped with various sensors to enable diverse emerging applications, e.g. locating sound-emitting targets. Multi-channel blind source separation algorithm has been applied into the unmanned aerial vehicles and micro aerial vehicles, where underdetermined mixture blind source separation is a challenging problem, i.e. the number of sources is more than the number of microphones. An optimization underdetermined blind source separation algorithm to separate the multi-channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. In the algorithm, firstly a hierarchical clustering to estimate channel as the mixing matrix initialization is employed, while using direction of arrival permutation algorithm to deal with the permutation alignment and update the mixing matrix using multiplication update method. Then the model parameters are estimated using improved expectation-maximization update rules for the fast convergence. Finally, the frequency-domain sources are estimated through Wiener filtering and time-domain sources are obtained via inverse short-time Fourier transform. Experimental results covering synthetic and real-recorded speech source mixtures show that the proposed algorithm achieves better separation results than the state-of-the-art methods. INTRODUCTIONUnmanned aerial vehicles (UAVs) have been playing an important role for 5G and beyond networks [1, 2], which can be equipped with microphones or cameras, and have been used to execute critical real-time tasks in our daily lives. For example, multi-rotor micro aerial vehicles (MAVs) can be equipped with microphones to locate sound-emitting targets. However, the received data collected by the MAVs are complex and changeable, which need to be processed for further real-time data analysis. Additionally, the microphones move together with the MAV, thus leading to a dynamic acoustic mixing network. To deal with this problem, blind source separation (BSS) method is separating source signals from multi-channel observations of mixture signals without any prior information on chan-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization

Cited by 15 publications

References 36 publications

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

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

Effective Channel Order Determination Algorithm for Convolutive Blind Channel Identification

Multi‐channel underdetermined blind source separation for recorded audio mixture signals using an unmanned aerial vehicle

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