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An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

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Abstract

Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

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Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No. 2011–0017941)

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, South Korea

    Mohammad A. Haque & Jong-Myon Kim

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  1. Mohammad A. Haque
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  2. Jong-Myon Kim
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Correspondence to Jong-Myon Kim.

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Haque, M.A., Kim, JM. An analysis of content-based classification of audio signals using a fuzzy c-means algorithm. Multimed Tools Appl 63, 77–92 (2013). https://doi.org/10.1007/s11042-012-1019-y

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