A Novel Underground Pipeline Surveillance System Based on Hybrid Acoustic Features

Abstract

Underground pipeline network suffers severe damages caused by construction machines used in the rapid urbanization development and construction, leading to profound impact to people’s life. An intelligent construction machinery classification (CMC) system thus becomes important to urban security and smart city engineering. Conventional methods are always problematic in one way or another. For instance, the one-dimensional speech features based recognition algorithms are usually less discriminative in the complex urban acoustic environment, and the two-dimensional spectrogram based convolution neural network (CNN) suffers a long model learning time and is less robust to the background noises. To address these deficiencies, a novel CMC system based on new hybrid acoustic features is presented. Two novel acoustic feature extraction methods are developed, where the first explores the concentrations of the Mel-frequency spectrogram (MFS) using the statistics of the binary MFS (SBMS) and the second characterizes the information entropy sequence feature of the binary MFS (IESF) based on the pulse-coupled neural network (PCNN) model. Then, the hybrid features of SBMS/IESF and the Linear Prediction Cepstral Coefficients (LPCC)/Mel-Frequency Cepstral Coefficients (MFCC) are further studied. A database consisting of 84,900 acoustic samples from 6 typical construction machines, horns of vehicles and other urban noises is recorded for experiment and performance validation. The comparisons with many state-of-the-art algorithms demonstrate the superiority of the proposed method.