Abstract
The purpose of automatic audio classification is to meet the rising need for efficient multimedia content management. This paper proposes a robust audio classification approach that classifies audio streams into one of five categories (speech, music, speech with music, speech with noise, and silence). The proposed method is composed of two steps: efficient audio feature extraction and audio classification using genetic algorithm-based fuzzy c-means. Experimental result indicates that the proposed classification approach achieves higher than 96.16 % in terms of classification accuracy.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2013R1A2A2A05004566 and NRF-2012R1A1A2043644).
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Kang, M., Kim, JM. (2014). Audio Classification Using GA-Based Fuzzy C-Means. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_47
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DOI: https://doi.org/10.1007/978-94-017-8798-7_47
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