• DocumentCode
    667473
  • Title

    Detection and classification of acoustic scenes and events: An IEEE AASP challenge

  • Author

    Giannoulis, Dimitrios ; Benetos, Emmanouil ; Stowell, Dan ; Rossignol, Mathias ; Lagrange, Mathieu ; Plumbley, Mark D.

  • Author_Institution
    Centre for Digital Music, Queen Mary Univ. of London, London, UK
  • fYear
    2013
  • fDate
    20-23 Oct. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This paper describes a newly-launched public evaluation challenge on acoustic scene classification and detection of sound events within a scene. Systems dealing with such tasks are far from exhibiting human-like performance and robustness. Undermining factors are numerous: the extreme variability of sources of interest possibly interfering, the presence of complex background noise as well as room effects like reverberation. The proposed challenge is an attempt to help the research community move forward in defining and studying the aforementioned tasks. Apart from the challenge description, this paper provides an overview of systems submitted to the challenge as well as a detailed evaluation of the results achieved by those systems.
  • Keywords
    audio signal processing; reverberation; IEEE AASP; acoustic events classification; acoustic events detection; acoustic scenes classification; acoustic scenes detection; complex background noise; human-like performance; monophonic audio; polyphonic audio; reverberation; Acoustics; Event detection; Hidden Markov models; MATLAB; Measurement; Signal processing; Support vector machines; Computational auditory scene analysis; acoustic event detection; acoustic scene classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications of Signal Processing to Audio and Acoustics (WASPAA), 2013 IEEE Workshop on
  • Conference_Location
    New Paltz, NY
  • ISSN
    1931-1168
  • Type

    conf

  • DOI
    10.1109/WASPAA.2013.6701819
  • Filename
    6701819