TY - JOUR
T1 - Predominant environmental noise classification over sound mixing based on source-specific dictionary
AU - López-Pacheco, María Guadalupe
AU - Sánchez-Fernández, Luis Pastor
AU - Molina-Lozano, Herón
AU - Sánchez-Pérez, Luis Alejandro
N1 - Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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.
AB - 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.
KW - Audio classification
KW - Environmental mixture signal
KW - Predominant source
KW - Signal decomposition
KW - Urban noise
UR - http://www.scopus.com/inward/record.url?scp=84973467704&partnerID=8YFLogxK
U2 - 10.1016/j.apacoust.2016.05.020
DO - 10.1016/j.apacoust.2016.05.020
M3 - Artículo
SN - 0003-682X
VL - 112
SP - 171
EP - 180
JO - Applied Acoustics
JF - Applied Acoustics
ER -