Acoustics recognition of construction equipments based on LPCC features and SVM

To provide the reliability of power supply and ensure the personal security, cables in urban city are usually paved underground in nowadays. However, according to the statistical report from State Grid, around 53.4% of cable breakages are caused by external damages from 2009 to 2011. Among these external cable vandalisms, construction equipments are the main damage sources, including impact hammer, cutting machine, grab excavator, etc. Thus, designing a surveillance system which can automatically detect such potential destroy is highly desired. In this paper, we investigate the automatic recognition system based on classifying the acoustic waves generated by the three equipments. In the proposed recognition framework, the linear prediction cepstral coefficients (LPCC) for acoustic waves generated by the three equipments are extracted in small frames. These LPCC features are then fed to the support vector machine classifier for training and testing. To show the efficiency of the recognition system, real recorded acoustic waves are collected in varies construction using a cross microphone array for experiments. Experiments show that the recognition algorithm we developed is efficiency.