A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

Ning, Fangli; Liu, Zhe; Song, Jiahao; Pan, Feng; Han, Pengcheng; Wei, Juan

doi:10.1186/s10033-020-00504-9

Cited by 2 publications

(1 citation statement)

References 48 publications

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gas is one of the basic requirements for the safe operation of substations.Traditional leakage detection methods are mainly classified as mechanism-driven and data-driven methods. Some of these include the bubble method [4], pressure-flow method [5], infrared thermography [6], acoustic method and so on [7]. Most of the current methods for sulfur hexafluoride gas detection use mechanism-driven methods, and its physical properties establish the prediction model.

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confidence: 99%

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

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Model’s generalization and feature selection are always two challenging problems for gas leakage detection. This paper presents a method for gas leakage detection based on Hybrid-Feature-Selection-Stacking ensemble learning (HFS-Stacking), which fuses the Hybrid Feature Selection and Stacking ensemble learning. Firstly, a hybrid feature selection algorithm (HFS), which combines SFS-SVM, SFS-KNN, RFE-RF, RFE-XGB, and MIC algorithms, is proposed to select the optimal feature subset from multiple commonly used acoustic signal features; Then, SVM, KNN, random forest and XGBoost are designed as base learners in the stacking integration framework, which increase the generalization ability of the model. XGBoost is used as the meta-learner to output the classification results. The proposed gas leakage detection method gets the optimal subset of features and speeds of the subsequent model. Meanwhile, the constructed model can effectively improve the indicator of ,, and, and has a good generalization ability. The experimental results show that the HFS-Stacking algorithm using fewer features can effectively improve the training speed, accuracy, F1-score, AUC value and recall rate, and also has better robustness.

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APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

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Deep Learning-Based Dereverberation for Sound Source Localization with Beamforming

Zhai,

Ning,

Hou

et al. 2023

J. Theor. Comp. Acout.

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In this work, an algorithm that combines deep-learning based dereverberation and beamforming is proposed for sound source localization in the reverberant environment. The contribution is combining deep-learning based dereverberation and beamforming together. Through deep learning, the proposed algorithm can directly achieve dereverberation of the cross-spectral matrix of microphone array measurement signals in the reverberant environment, which can overcome the challenge of requiring multiple measurements like average beamforming. In this way, the proposed algorithm can be applied to the reverberant environment that requires efficient sound source localization. At the same time, the network of deep-learning based dereverberation is trained directly using the cross-spectral matrix of signals collected by microphone arrays, which can overcome the challenge of requiring prior knowledge of reflective surfaces like empirical dereverberation beamforming. In this way, the proposed algorithm can be applied to the reverberant environment composed of walls with unknown sound reflection coefficients. Both specular reflections, diffuse reflections, and background noise are considered in the reverberant environment. The cross-spectral matrix of microphone array measurement signals in the reverberant environment is first fed into the U-net for dereverberation and then combined with beamforming for sound source localization. The results in the test set show that the proposed algorithm can eliminate the influence of reverberation on sound source localization. The applicability limitation results of the proposed algorithm show the proposed algorithm’s strong robustness to unseen sound reflection coefficient, unseen SNR, and expandability to some degree for unseen source location. However, the proposed algorithm cannot provide stable and accurate sound source localization results under unseen room geometry. The proposed algorithm has higher spatial resolution and smaller errors than the average beamforming and the empirical dereverberation beamforming.

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A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

Cited by 2 publications

References 48 publications

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

Deep Learning-Based Dereverberation for Sound Source Localization with Beamforming

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