Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning

Billel Alla Eddine Bencharif; Ibrahim Ölçer; Erkan Özkan; Berke Cesur; Cem Aygül

doi:10.1117/12.2581696

8 November 2020 Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning

Billel Alla Eddine Bencharif, Ibrahim Ölçer, Erkan Özkan, Berke Cesur, Cem Aygül

Author Affiliations +

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115251S (2020) https://doi.org/10.1117/12.2581696
Event: SPIE Future Sensing Technologies, 2020, Online Only

Conference Poster

Abstract

This work aims at the detection and classification of Distributed Acoustic Sensor (DAS) acquired acoustic signals. We obtained the data by probing an optical fiber with light pulses and gauging the Rayleigh backscatter. Said data contains four different classes; Walking, Shovel and Pick digging as well as Hammer hitting. We first proceed by detecting the event and its location along the fiber and extracting it from the random noise using Spiked Random Matrix Theory (RMT) models, namely Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions. We then label the datasets accordingly and proceed with the classification process using machine learning algorithms. For this, we test and evaluate Convolutional Neural Networks (CNN), which has been proven to provide high accuracies in similar studies, taking the spectrograms of the signals as our network’s input. We conclude by providing the performance of our CNN architecture and propose a few options to further improve the performance of the model.

Citation Download Citation

Billel Alla Eddine Bencharif, Ibrahim Ölçer, Erkan Özkan, Berke Cesur, and Cem Aygül "Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251S (8 November 2020); https://doi.org/10.1117/12.2581696

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