Novel denoizing method for partial discharge signals using singular value decomposition and spectral subtraction

Xu, Yifan; Yan, Jing; Wang, Yanxin; He, Ruixin; Wang, Jianhua; Geng, Yingsan

doi:10.1049/smt2.12134

Cited by 5 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scientists performed voice restoration and encoding using SS-VAD and LPC, both depending on a noise reduction approach, to minimize unexpected noise [33]. By applying spectrum subtraction, the researchers in [34] provided an exclusive denoising technique to remove partially discharged signals. SS tool is one approach suggested by [35] for eliminating noise in noisy audio signals in their frequency space.…”

Section: Spectral Subtraction Ltermentioning

confidence: 99%

Discrete Wavelet Transform and Spectral Subtraction Based Speech Enhancement Algorithm for Hearing Aid Application

Iqbal,

Zhang,

Geng

et al. 2024

Preprint

View full text Add to dashboard Cite

Hearing aids are small electronic devices intended to help those with hearing loss improve their hearing ability with the use of advanced audio signal processing techniques and technologies. Usually, in hearing aids, a set of speech enhancement methods are utilized to improve speech signal quality in low signal-to-noise ratio (SNR) environments. In speech processing, Discrete Wavelet Transform (DWT) algorithm and Spectral Subtraction (SS) filter are some of the most commonly used methods today when handling background noise in hearing aids. However, these speech enhancement systems have drawbacks like in DWT, the selective thresholding face problems when applying to distinct types of noise with different frequencies and time scales, and spectral subtraction has a problem with music noise, which affects the denoising performance. In this paper, the Spectral Subtraction (SS) filter and DWT speech enhancement methods are combined to deal with problem faced by DWT selective thresholding techniques against noise of different frequencies and time scales and the musical noise faced by the spectral subtraction filter. We first used spectral subtraction to reduce average noise intensity and then added DWT thresholding for reducing the background noise further. The contemporary methods evaluated using English speech signals taken from CSTR VCTK Corpus database. The objective metrics used to evaluate proposed speech enhancement systems are MSE, SNR, PESQ, and STOI. The proposed speech enhancement algorithm is applied for noise reduction and then compared with conventional algorithms. Experimental results prove that using of the proposed speech enhancement algorithm reduces background noise, improves the SNR of an original speech signal, and improves the final noise reduction performance.

show abstract

Section: Spectral Subtraction Ltermentioning

confidence: 99%

Discrete Wavelet Transform and Spectral Subtraction Based Speech Enhancement Algorithm for Hearing Aid Application

Iqbal,

Zhang,

Geng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Singular Value Decomposition. The initial step necessitates the formation of a Hankel matrix [15].The construction is shown below: Let s be a noisy PD signal with noise. Transform s into a Hankel matrix as shown in the following equation.…”

Section: Denoising Algorithmmentioning

confidence: 99%

Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding

Wang,

Yao,

Zhang

et al. 2024

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

Partial discharge evaluation is a principal method for assessing insulation conditions in power transformers. Traditional singular value decomposition (SVD) approaches, however, face issues like high residual noise and loss of signal details in white noise suppression. This article introduces an advanced denoising algorithm integrating SVD, variational mode decomposition (VMD), and wavelet thresholding to effectively address mixed noise in on-site power transformer assessments. The algorithm initially employs SVD to suppress mixed noise, specifically targeting narrowband interference by decomposing the noisy signal and nullifying the corresponding singular values. Post-SVD, the signal is further processed through VMD, with its modal components refined via wavelet thresholding. The final reconstruction of these denoised components effectively eliminates white noise. Applied to an input signal with a signal-to-noise ratio of -27.593 dB, the proposed method achieves a postdenoising ratio of 13.654 dB. Comparative analysis indicates its superiority over existing algorithms in mitigating white noise and narrowband interference and more accurately restoring the partial discharge signal.

show abstract

“…Currently, some traditional signal denoising techniques are employed for noise reduction in PD signals, including Wavelet Transform (WT) [4], Empirical Modal Decomposition (EMD) [5], and SVD [6]. WT is recognized for its proficient time-frequency analysis capabilities, but selecting an optimal wavelet basis for signal decomposition remains challenging, and the choice of wavelet basis and decomposition level significantly affects the denoising outcome [7].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

Zhong,

Liu,

2024

Applied Sciences

View full text Add to dashboard Cite

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.

show abstract

Novel denoizing method for partial discharge signals using singular value decomposition and spectral subtraction

Cited by 5 publications

References 25 publications

Discrete Wavelet Transform and Spectral Subtraction Based Speech Enhancement Algorithm for Hearing Aid Application

Discrete Wavelet Transform and Spectral Subtraction Based Speech Enhancement Algorithm for Hearing Aid Application

Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

Contact Info

Product

Resources

About