Identifying cutting sound characteristics in machine tool industry with a neural network
Yudai Ota, Bogdan M. Wilamowski · 2002
This paper presents a method for identifying cutting sound characteristics for machine tool industry based on a robust time-variant sound recognition system. The sound signal is compressed using linear prediction analysis method and then recognized by an artificial neural network. The procedure taken here is based on the following: (1) extraction of time-variant spectral features (i.e., raw data of sound), (2) characterization of each sample by observing the autocorrelation coefficients and reflection coefficients of the sampled data, and (3) training of an artificial neural network to identify extracted sound samples. The proposed technique is shown to be very effective, accurate, and powerful in performing sound data identification.