Sonar echo signal processing based on Convolution Blind source separation

Zhang, Yan; Sun, Shengkai; Liu, Yue; Wang, Jiaqi

doi:10.1109/icicsp50920.2020.9232058

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…FMBD uses kurtosis as the control function and blind separation of signals by the successive extraction method, which is applicable to both determined and underdetermined blind source separation. Yan et al adopted the JADE convolution BSS algorithm in the frequency domain to achieve the separation of active sonar echo signals in a marine environment full of complex noise and reverberation [17].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis

Yin

2023

Sensors

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Composite fault diagnosis of rolling bearings is very challenging work, especially when the characteristic frequency ranges of different fault types overlap. To solve this problem, an enhanced harmonic vector analysis (EHVA) method was proposed. Firstly, the wavelet threshold (WT) denoising method is used to denoise the collected vibration signals to reduce the influence of noise. Next, harmonic vector analysis (HVA) is used to remove the convolution effect of the signal transmission path, and blind separation of fault signals is carried out. The cepstrum threshold is used in HVA to enhance the harmonic structure of the signal, and a Wiener-like mask will be constructed to make the separated signals more independent in each iteration. Then, the backward projection technique is used to align the frequency scale of the separated signals, and each fault signal can be obtained from composite fault diagnosis signals. Finally, to make the fault characteristics more prominent, a kurtogram was used to find the resonant frequency band of the separated signals by calculating its spectral kurtosis. Semi-physical simulation experiments are conducted using the rolling bearing fault experiment data to verify the effectiveness of the proposed method. The results show that the proposed method, EHVA, can effectively extract the composite faults of rolling bearings. Compared to fast independent component analysis (FICA) and traditional HVA, EHVA improves separation accuracy, enhances fault characteristics, and has higher accuracy and efficiency compared to fast multichannel blind deconvolution (FMBD).

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis

Yin

2023

Sensors

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“…It exploits the feature that mutual accumulation is always zero when signals are independent and builds multiple fourth-order accumulation matrices for multivariate data. Lastly, the mentioned cumulant matrices are jointly diagonalized to solve for the final separated signals [27,28]. For single-channel signal, CNMF+JADE can effectively separate the overlapped speech including the target speaker.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unsupervised Single‐Channel Speech Separation Algorithm for Processing Speech Sensor Signals

Jiang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

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Sonar echo signal processing based on Convolution Blind source separation

Cited by 2 publications

References 11 publications

Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis

Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis

An Improved Unsupervised Single‐Channel Speech Separation Algorithm for Processing Speech Sensor Signals

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