Acoustic detection and classification of river boats

Amir Averbuch*, Valery Zheludev, Pekka Neittaanmäki, Pekka Wartiainen, Kari Huoman, Kim Janson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.

Original languageEnglish
Pages (from-to)22-34
Number of pages13
JournalApplied Acoustics
Volume72
Issue number1
DOIs
StatePublished - Jan 2011

Keywords

  • Best Discriminant Basis
  • Classification and Regression Trees (CART)
  • Classifiers
  • Hydro-acoustic signature
  • Linear Discriminant Analysis (LDA)
  • Nearest neighbor (NN) classifier
  • Wavelet packet

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