What are the current techniques used for unsupervised railway sound event identification and localization?
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Current techniques for unsupervised railway sound event identification and localization involve advanced methods like deep learning models and image reconstruction-based anomaly detection. Deep learning models, including convolutional neural networks (CNNs) and artificial neural networks, are utilized for accurate identification of sound sources along the rail structure . Additionally, image-inpainting anomaly detection networks (IADN) are proposed for anomaly classification and localization, achieving high precision and recall rates in detecting anomalies between platform doors and trains in railway settings . These techniques leverage sophisticated algorithms to enhance the accuracy of identifying and localizing sound events in railway environments, showcasing significant advancements in the field of unsupervised railway sound event detection and localization.
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| The paper proposes a deep learning approach using 2DCNN for acoustic emission source localization in rail sections, achieving 94.79% accuracy in zone identification and 73.12-79.37% in location classification. | |
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28 Mar 2023 | Not addressed in the paper. |
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