کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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760744 | 1462397 | 2016 | 10 صفحه PDF | دانلود رایگان |
This paper presents a methodology to classify predominant urban acoustic sources in real mixed signals. This is based on a source-specific dictionary with atoms in the time–frequency domain using the Orthogonal Matching Pursuit (OMP) algorithm and identifying the class through a proposed selection criterion with a dynamic number of iterations involving a lower algorithm complexity. Several time–frequency atoms were evaluated considering retained energy and relative error to build a source-specific dictionary in the relevant classes. The source-specific dictionary has better results up to 7% in retained energy than to use an individual dictionary such as based on wavelet or Gabor functions, improving classification of predominant sources over sound mixing up to 9% compared to using standard dictionaries. Experimental results on classification are applied to mixture inter-class signals of two or more sources recorded by a real permanent monitoring system in an urban soundscape. The classification performance has successfully achieved identifying a predominant source in real inter-class mixtures of urban soundscapes.
Journal: Applied Acoustics - Volume 112, November 2016, Pages 171–180