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.
Original language | English |
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Journal | Multimedia Tools and Applications |
Volume | 63 |
Issue number | 1 |
Pages (from-to) | 77-92 |
Number of pages | 16 |
ISSN | 1380-7501 |
DOIs | |
Publication status | Published - 1 Mar 2013 |
Keywords
- Audio segmentation and classification
- Database retrieval
- Fuzzy c-means algorithm
- Multimedia