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Robust Industrial Machine Sounds Identification Based on Frequency Spectrum Analysis

  • Antoni Grau
  • Yolanda Bolea
  • Manuel Manzanares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

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.

Keywords

wavelets Fast Fourier Transformation non-speech sound 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antoni Grau
    • 1
  • Yolanda Bolea
    • 1
  • Manuel Manzanares
    • 1
  1. 1.Automatic Control Dept, Technical University of Catalonia UPC, BarcelonaSpain

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