Feature extraction for acoustic classification of small aircraft

Alexander Yakubovskiy, Hady Salloum, Alexander Sutin, Alexander Sedunov, Nikolay Sedunov, David Masters

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Standard approach to aircraft classification in acoustic sensor networks is based on propeller blades rotation harmonics and fundamental frequency analysis. However, propeller blades induced sound is not the only sound producing mechanism. As a result, aircraft of different classes (small plane, helicopter, and ultralight aircraft) often provide similar acoustic signatures in the spectral domain. We present new feature extraction methods beyond the spectral peaks-based ones. First, general feature extraction considerations are reviewed. Then features are described including harmonic-based feature, a feature based on Lloyd's mirror multi-path propagation effect, and a feature based on sound spikes caused by propeller blades interaction with flow vortices. Experiments are provided to justify the efficiency of the proposed features for target classification.

Original languageEnglish
Title of host publication2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015
ISBN (Electronic)9781479974504
DOIs
StatePublished - 24 Nov 2015
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015 - New Paltz, United States
Duration: 18 Oct 201521 Oct 2015

Publication series

Name2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015

Conference

ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015
Country/TerritoryUnited States
CityNew Paltz
Period18/10/1521/10/15

Keywords

  • acoustic aircraft classification
  • classification decision tree
  • feature extraction
  • information fusion

Fingerprint

Dive into the research topics of 'Feature extraction for acoustic classification of small aircraft'. Together they form a unique fingerprint.

Cite this