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. © Springer-Verlag 2004.
CITATION STYLE
Bolea, Y., Grau, A., Pelissier, A., & Sanfeliu, A. (2004). Structural pattern recognition for industrial machine sounds based on frequency spectrum analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3287, 287–295. https://doi.org/10.1007/978-3-540-30463-0_35
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