In order to prevent damages to the underground pipelines, automatic, continuous, and low-cost monitoring systems are becoming indispensable. Recent reports showed that most pipeline damage is caused by the third-party activities, which can be identified by detecting for dangerous construction equipments nearby, rather than the material failure and corrosion. This paper focuses on acoustically recognizing road cutters since they prelude most construction activities in modern cities. In this paper, a novel monitoring mechanism is proposed for sound-based pipeline monitoring system. Instead of the conventional approach of using overlapped frames, this paper proposes segmenting sound into separate frames, with an interval between every two frames. After gathering sound samples, a tag stack decision maker is also proposed to make the final decision. Different features of two acoustic recognition technologies, Mel frequency cepstral coefficients (MFCCs) and linear prediction coding cepstrum (LPCC), were studied, tried, and compared. For the classifiers, both the Euclidean cepstral distance and one-class support vector machine (SVM) were studied. Real site experiments were conducted and the resulting data were analyzed. Recognition success rate at different conditions were studied and compared. Our results showed that MFCC feature performs the best. Cepstral Euclidean distance is simple but effective, while one-class SVM can help to extend the classification capabilities. The sound recognition technologies studied in this paper will be very useful for future pipeline monitoring systems to prevent accidental breakage, thus ensuring the safety of underground lifeline infrastructures.
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