期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：8
DOI：10.14569/IJACSA.2018.090820
出版社：Science and Information Society (SAI)
摘要：Acoustic complements is an important methodology to perceive the sounds from environment. Significantly machines in different conditions can have the hearings capability like smartphones, different software or security systems. This kind of work can be implemented through conventional or deep learning machine models that contain revolutionized speech identification to understand general environment sounds. This work focuses on the acoustic classification and improves the performance of deep neural networks by using hybrid feature extraction methods. This study improves the efficiency of classification to extract features and make prediction of cost graph. We have adopted the hybrid feature extraction scheme consisting of DNN and CNN. The results have 12% improvement from the previous results by using mix feature extraction scheme.
关键词：Acoustics; deep learning; machine learning; neural networks; audio sounds
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