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Pitch-density-based features and an SVM binary tree approach for multi-class audio classification in broadcast news

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Abstract

Audio classification is an essential task in multimedia content analysis, which is a prerequisite to a variety of tasks such as segmentation, indexing and retrieval. This paper describes our study on multi-class audio classification on broadcast news, a popular multimedia repository with rich audio types. Motivated by the tonal regulations of music, we propose two pitch-density-based features, namely average pitch-density (APD) and relative tonal power density (RTPD). We use an SVM binary tree (SVM-BT) to hierarchically classify an audio clip into five classes: pure speech, music, environment sound, speech with music and speech with environment sound. Since SVM is a binary classifier, we use the SVM-BT architecture to realize coarse-to-fine multi-class classification with high accuracy and efficiency. Experiments show that the proposed one-dimensional APD and RTPD features are able to achieve comparable accuracy with popular high-dimensional features in speech/music discrimination, and the SVM-BT approach demonstrates superior performance in multi-class audio classification. With the help of the pitch-density-based features, we can achieve a high average accuracy of 94.2% in the five-class audio classification task.

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Notes

  1. http://www.cctv.com.

  2. http://marsyas.sness.net/.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (60802085), the Program for New Century Excellent Talents in University (2008) supported by the Ministry of Education (MOE) of China, the Research Fund for the Doctoral Program of Higher Education in China (20070699015), the Natural Science Basic Research Plan of Shaanxi Province (2007F15) and the NPU Foundation for Fundamental Research (W018103).

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Authors and Affiliations

  1. Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Lei Xie, Zhong-Hua Fu & Yong Luo

  2. Media Computing Group, School of Creative Media, City University of Hong Kong, Kowloon, Hong Kong, China

    Wei Feng

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  1. Lei Xie
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  2. Zhong-Hua Fu
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  3. Wei Feng
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  4. Yong Luo
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Corresponding author

Correspondence to Lei Xie.

Additional information

Communicated by T. Haenselmann.

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Xie, L., Fu, ZH., Feng, W. et al. Pitch-density-based features and an SVM binary tree approach for multi-class audio classification in broadcast news. Multimedia Systems 17, 101–112 (2011). https://doi.org/10.1007/s00530-010-0205-x

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