Comparison of techniques for environmental sound recognition

  • Authors:
  • Michael Cowling;Renate Sitte

  • Affiliations:
  • School of Information Technology, Griffith University, Gold Coast Campus, PMB 50, Gold Coast Mail Centre, Queensland 9726, Australia;School of Information Technology, Griffith University, Gold Coast Campus, PMB 50, Gold Coast Mail Centre, Queensland 9726, Australia

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.