Predominant environmental noise classification over sound mixing based on source-specific dictionary |
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Authors: | María Guadalupe López-Pacheco Luis Pastor Sánchez-Fernández Herón Molina-Lozano Luis Alejandro Sánchez-Pérez |
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Institution: | Centro de Investigación en Computación – Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Gustavo A. Madero, México D.F. 07738, Mexico |
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Abstract: | 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. |
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Keywords: | Audio classification Urban noise Signal decomposition Predominant source Environmental mixture signal |
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