Abstract
In acoustic engineering, the types of material used in a room are basically one of the fundamental features that are essential in some of room acoustic parameters computation. This paper proposed an improved system to identify room material type from its surface photographic image. Data images of several room surfaces were collected for the system input. This improved system implements Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments for image extraction and hybrid Particle Swarm Optimization and back-propagation (PSO-BP) algorithm for classification. For comparison purpose, experiments using variations combination of GLCM and modified Zernike moments extraction as well as Levenberg-Marquardt, back-propagation neural network (BPNN), and PSO-BP algorithm were executed. By applying the proposed methods, the system accuracy increased around 30% compared to previous research. Moreover, the convergence attained during training was three times faster compared to BP algorithm. Thus using the new methods in identifying material surface images had positively improved the system in becoming more efficient and reliable.
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