Abstract
In order to discriminate and identify different industrial machine sounds corrupted with heavy non-stationary and non-Gaussian perturbations (high noise, speech, etc.), a new methodology is proposed in this article. From every sound signal a set of features is extracted based on its denoised frequency spectrum using Morlet wavelet transformation (CWT), and the distance between feature vectors is used to identify the signals and their noisy versions. This methodology has been tested with real sounds, and it has been validated with corrupted sounds with very low signal-noise ratio (SNR) values, demonstrating the method’s robustness.
Chapter PDF
Similar content being viewed by others
References
Mori, K., Kasashima, N., Yoshioha, T., Ueno, Y.: Prediction of Spalling on a Ball Bearing by Applying the Discrete Wavelet Transform to Vibration Signals. Wear 195(1-2), 162–168 (1996)
Roberts, S., Everson, R.: Independent Component Analysis: Principles and Practice. Cambridge Univ. Press, Cambridge, UK (2001)
Bolea, Y., Grau, A., Sanfeliu, A.: Non-speech Sound Feature Extraction based on Model Identification for Robot Navigation, 8 Iberoamerican Congress on Pattern Recognition. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 221–228. Springer, Heidelberg (2003)
Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. on Signal Processing 45(12), 3397–3415 (1993)
Donoho, D.-L.: De-noising by soft-thresholding. IEEE Trans. on Information Theory 33(7), 2183–2191 (1999)
Lin, J.: Feature Extraction of Machine Sound using Wavelet and its Application in Fault Diagnosis. NTD&E International 34, 25–30 (2001)
Goupilland, P., Grossmann, A., Morlet, J.: Cycle octave and related transforms in seismic signal analysis. Geoexploration 23, 85–102 (1984)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grau, A., Bolea, Y., Manzanares, M. (2007). Robust Industrial Machine Sounds Identification Based on Frequency Spectrum Analysis. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-76725-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76724-4
Online ISBN: 978-3-540-76725-1
eBook Packages: Computer ScienceComputer Science (R0)