An Underdetermined Blind Source Separation Method with Application to Modal Identification

Yu, Gang

doi:10.1155/2019/1637163

Cited by 12 publications

(13 citation statements)

References 27 publications

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“…Since the "boundary effect" of STFT has an impact on the separation performance [10], we selected the Kaiser window (β = 6.865) as the window function. The evaluation criteria of separation performance were applied in our simulations, which include the source to distortion ratio (SDR), the source to interferences ratio (SIR), the source to artifacts ratio (SAR) [23], and the separated bit error rate (BER).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…To obtain bitstream information, the coherent demodulation method was used to demodulate the separated source signals. The mixed signals were separated by the SCA algorithm proposed in this paper and the literature [8] [10]. The benchmark algorithm in [8] belonged to the classic two-stage method, and [10] used the TF masking method to separate the source signal.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The mixed signals were separated by the SCA algorithm proposed in this paper and the literature [8] [10]. The benchmark algorithm in [8] belonged to the classic two-stage method, and [10] used the TF masking method to separate the source signal.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Ideally, the reconstruction of the source signal is achieved by inverse short time Fourier transform (ISTFT) in the frequency band [f i − ∆, f i + ∆] [10]. However, the traditional clustering method is only a kind of hard segmentation of power points to obtain the TF binary mask.…”

Section: Source Signal Recovery By Tf Soft Maskmentioning

confidence: 99%

“…The other is the TF masking method, i. e., the source signal is separated by designing a TF mask matrix. Yu [10] firstly detected the peak energy point of the mixed signals, and then calculated the cosine distance between all energy points and the peak energy point, afterward obtained the binary mask based on a threshold to realize the reconstruction of the vibration signal; Cobos [11] proposed a maximum a posteriori decision criterion to design a binary mask matrix for the separation of mixed speech signals; Kumar [12] first detected the active single-source points by calculating the mixing ratio, and then found the cluster centers by a dynamic routing algorithm as a way to design a binary mask. The above algorithms have achieved satisfactory performance in the fields of modality recognition and speech separation, etc.…”

Section: Introductionmentioning

confidence: 99%

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An underdetermined convolutional blind separation algorithm for time-frequency overlapped wireless communication signals with unknown source number

Zheng

et al. 2022

Preprint

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It has been challenging to separate the time-frequency (TF) overlapped wireless communication signals with unknown number of sources in underdetermined cases. 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. Afterwards, 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. In this paper, 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 benchmark algorithms.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Source Signal Recovery By Tf Soft Maskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An underdetermined convolutional blind separation algorithm for time-frequency overlapped wireless communication signals with unknown source number

Zheng

et al. 2022

Preprint

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Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

Jialu¹,

Zhou²,

Liu³

et al. 2023

Meas. Sci. Technol.

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In the production practice, the signal gathered by sensor often includes strong ambient noise, and its composition is complex. Focusing on the trouble that traditional methods are difficult to separate and extract fault frequency from strong background noise, a novel compound fault blind extraction means based on improved sparse component analysis (ISCA) and improved maximum correlation kurtosis deconvolution (IMCKD)-named ISCA‐IMCKD- is suggested. Initially, the signal that the sensor has collected is shifted into time-frequency area signal by short-time Fourier transform (STFT). In addition, the single source domain characteristic data is screened by improved single source point detection (ISSPD) to decide the number of sources. Secondly, ISCA method is optimized by using cosine distance improved fuzzy C-means clustering, which is utilized to further process the characteristic data to calculate the mixing matrix. Moreover, estimated source signal is initially extracted according to the membership degree of clustering results. Finally, estimated source signal is shift into the time area by inverse STFT transform, and the IMCKD is employed to enhance the characteristics of the projected source signals. Meanwhile, initially estimated source signal is completely separated, and the defect frequency of the composite faults are finally extracted by envelope analysis. Simulation experiments and measured data are employed to certify the viability of the proposed means. The defect detection of rolling bearings is finished while the time cost is significantly saved.

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Blind Source Separation Based on Improved Wave-U-Net Network

Lan,

Jiang,

Zhang

et al. 2023

IEEE Access

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With the development and widespread application of voice interaction technology, it has become crucial to improve the accuracy of blind source separation technology. In order to further enhance the separation results of vocal and accompaniment, this paper proposes an improved Wave-U-Net model. Based on the skip connection of the Wave-U-Net model, we propose a segmented attention module (SAM) consisting of a spatial attention module (SPAM) and a channel attention module (CAM) to replace the skip connections in this model to solve the semantic gap caused by feature concatenation. Furthermore, we replace the 1D convolution layer of the bottleneck layer in this model with an atrous spatial pyramid pooling (ASPP) module. The purpose is to increase the receptive field and obtain multi-scale features at the same time, thereby improving the speech separation performance of the model. We conduct experimental tests in the Musdb18 dataset, and analyze the performance of the model using the SDR, SIR and SAR evaluation indicators. The research results denote that compared with the Wave-U-Net network that only uses feature concatenation, the SDR values of the restored vocal and restored accompaniment are increased by 4.229dB and 4.626dB, respectively, and the separation performance is better than some existing baseline models.

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An Underdetermined Blind Source Separation Method with Application to Modal Identification

Cited by 12 publications

References 27 publications

An underdetermined convolutional blind separation algorithm for time-frequency overlapped wireless communication signals with unknown source number

An underdetermined convolutional blind separation algorithm for time-frequency overlapped wireless communication signals with unknown source number

Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

Blind Source Separation Based on Improved Wave-U-Net Network

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