Structural pattern recognition for industrial machine sounds based on frequency spectrum analysis

Abstract

In order to discriminate different industrial machine sounds contaminated with perturbations (high noise, speech, etc.), a spectral analysis based on a structural pattern recognition technique is proposed. This approach consists of three steps: 1) to de-noise the machine sounds using the Morlet wavelet transform, 2) to calculate the frequency spectrums for these purified signals, and 3) to convert these spectrums into strings, and use an approximated string matching technique, finding a distance measure (the Levenshtein distance) to discriminate the sounds. This method has been tested in artificial signals as well as in real sounds from industrial machines.