• DocumentCode
    1885391
  • Title

    Eco-Environmental Sound Classification Based on Matching Pursuit and Support Vector Machine

  • Author

    Li, Yong ; Li, Ying

  • Author_Institution
    Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China
  • fYear
    2010
  • fDate
    25-26 Dec. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Research on various eco-environmental sounds is very important for people to understand a particular area. However, eco-environmental sounds have many specific properties such as the diversity, high background noise and non-stationary structure which make many traditional audio features hard to characterize them accurately. In this paper, a novel feature extraction technique based on Matching Pursuit algorithm is adopted to obtain proper features which can describe the specific properties of eco-environment sounds accurately. In order to further improve the performance of our classification system, we make use of the support vector machine to automatically classify the eco-environmental sounds into seven classes. The experimental results show that our system is effective to classify the environmental sounds quickly and accurately.
  • Keywords
    acoustic signal processing; audio signal processing; ecology; environmental science computing; feature extraction; pattern matching; signal classification; support vector machines; audio features; eco-environmental sound classification; feature extraction technique; matching pursuit algorithm; support vector machine; Accuracy; Classification algorithms; Feature extraction; Kernel; Matching pursuit algorithms; Mel frequency cepstral coefficient; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
  • Conference_Location
    Wuhan
  • ISSN
    2156-7379
  • Print_ISBN
    978-1-4244-7939-9
  • Electronic_ISBN
    2156-7379
  • Type

    conf

  • DOI
    10.1109/ICIECS.2010.5677677
  • Filename
    5677677