Abstract
Automatic audio information retrieval is facing great challenge due to the advances of information technology, more and more digital audio, images and video are being captured, produced and stored. To develop an automatic audio signal classification for a large dataset, building audio classifier is still challenging in existing work. In this proposed system we combine the two classifiers, SVM and decision tree, to classify the video information. To classify the audio information by using decision tree, the SVM is applied as a decision for feature selection. The aim is to achieve high accuracy in classifying of mixed types audio by combining two types of classifiers. In this proposed system four audio classes are considered and this classification and analysis is intended to analyze the structure of the sports video. Soccer videos are experimented in this system and experimental study indicates that the proposed framework can produce satisfactory results.
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References
Rabaoui, A., Kadri, H., Lachiri, Z., Ellouze, N.: One-Class SVMs Challenges in Audio Detection and Classification Applications: Hindawi Publishing Corporation. EURASIP Journal on Advances in Signal Processing 2008, Article ID 834973, 14 pages (2008)
Chen, L., Gunduz, S., Ozsu, M.: Mixed Type Audio Classification with Support Vector Machine. In: Proceedings of the 2006 IEEE International Conference on Multimedia and Expo., ICME, pp. 781–784 (2006)
Bai, L., Lao, S.Y., Lia0, H.X., Chen, J.Y.: Audio Classification and Segmentation for Sports Video Structure Extraction Using Support Vector Machine. In: IEEE, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, ICMLC, China, pp. 3303–3307 (2006)
Kim, H.G., Jeong, J., Kim, J.H., Kim, J.: Real-Time Highlight Detection in Baseball Video for TVs with Time-Shift Function. IEEE Trans. on Consumer Elec. 54(2), 831–838 (2008)
Xu, M., Xu, C., Duan, L., Jin, J., Luo, S.: Audio Keywords Generation for Sports Video Analysis. ACM Tomccap 4(2) (2008)
Barbedo, J.G.A., Lopes, A.: A Robust and Computationally Efficient Speech/Music Discriminator. Journal of the Audio Engineering Society 54(7/8), 571–588 (2006)
Scheirer, E., Slancy, M.: Construction and Evaluation of a Robust Multi-feature Speech/Music Discriminator. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1331–1334. IEEE, Los Alamitos (1997)
Zhang, T., Kuo, J.: Audio Content Analysis for Online Audio-Visual Data Segmentation and Classification. Transactions on Speech and Audio Processing 9(4), 441–457 (2001)
Xu, M., Maddage, N.C., Xu, C.S., Kankanhalli, M., Tian, Q.: Creating Audio Key-words for Event Detection in Soccer Video. In: Proceedings of International Conference on Multimedia and Expo., ICME, Japan, vol. 2, pp. 281–184 (2003)
Kumari, R.S.S., Sugumar, D., Sadasivam, V.: Audio Signal Classification Based on Optimal Wavelet and Support Vector Machine. In: IEEE, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), vol. 13(5) (2007)
Lu, L., Zhang, H.J., Li, S.Z.: Content-based Audio Classification and Segmentation by Using SVM: Multimedia Systems. Journal Article, 482–492 (2003), 2003-Springer Digital Object Identifier (DOI) 10. 1007/s00530-002-0065-0
Jain, A.K.: Statistical Pattern Recognition: a review. IEEE Trans. PAMI 2(1), 4–37 (2001)
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Lin, K.Z., Pwint, M. (2010). Structuring Sport Video through Audio Event Classification. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_44
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DOI: https://doi.org/10.1007/978-3-642-15702-8_44
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