Acoustics recognition of excavation equipment based on MF-PLPCC features and RELM

With the development of urbanization, the underground pipelines play an important role in ensuring the basic conditions of citizen life. However, construction activities are continuously increasing in the mainland of China nowadays, which lead to serious damages and threatens to the underground pipelines by excavation equipment. To this end, we focus on developing an automatically monitoring system for underground pipeline network protection in this paper. The acoustic characteristics of excavation equipment are first analyzed. Then, a recognition framework combining with the feature extraction algorithm of Mel-Frequency Linear Prediction Cepstral Coefficients (MF-PLPCC) and the classification algorithm of Regularized Extreme Learning Machine (RELM) is developed for excavation equipment. The MF-PLPCC processing includes the use of Mel filter bank that simulates the acoustic perception of human ear to different frequency, and the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC. Experimental results demonstrate that the method of this paper provides substantial improvements in recognition accuracy comparing to other state-of-the-art methods.