• DocumentCode
    13731
  • Title

    Acoustic Scene Classification: Classifying environments from the sounds they produce

  • Author

    Barchiesi, Daniele ; Giannoulis, Dimitrios ; Stowell, Dan ; Plumbley, Mark D.

  • Author_Institution
    Sch. of Electron. Eng. & Comput. Sci., Queen Mary, Univ. of London, London, UK
  • Volume
    32
  • Issue
    3
  • fYear
    2015
  • fDate
    May-15
  • Firstpage
    16
  • Lastpage
    34
  • Abstract
    In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods.
  • Keywords
    Gaussian processes; acoustic signal processing; maximum likelihood estimation; mixture models; signal classification; ASC techniques; acoustic scene classification; environmen classification; statistical significance test; Acoustics; Classification algorithms; Feature extraction; Frequency measurement; Hidden Markov models; Image analysis; Signal processing algorithms;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2014.2326181
  • Filename
    7078982