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
Link To Document :
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