Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization. Thus, designing a round-the-clock intelligent surveillance system has become crucial and urgent. In this study, we develop an acoustic signal-based excavation device recognition system for underground pipeline protection. The front-end hardware system is equipped with an acoustic sensor array, an Analog-to-Digital Converter (ADC) module (ADS1274), and an industrial processor Advanced RISC Machine (ARM) cortex-A8 for signal collection and algorithm implementation. Then, a novel Statistical Time-Frequency acoustic Feature (STFF) is proposed, and a fast Extreme Learning Machine (ELM) is adopted as the classifier. Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features. In addition, the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.
UNDERGROUND PIPELINE SURVEILLANCE WITH AN ALGORITHM BASED ON STATISTICAL TIME-FREQUENCY ACOUSTIC FEATURES