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This paper analyses the consistency of a set of MPEG-7 low level audio descriptors when the input audio stream has previously been compressed with a lossy compression algorithm. The analysis results show that lossy compression has a detrimental effect on the integrity of practical search and retrieval schemes that utilize the low level audio descriptors. Methods are then proposed to reduce the detrimental effects of compression in searching schemes. These proposed methods include improved searches, switched adaptive scalar and vector prediction, and other prediction schemes based on machine learning principles. Of the proposed schemes the results indicate that searching which incorporates previous and future frames combined with machine learning based prediction best nullifies the effects of compression. However, future scope is identified to further improve the reliability of the MPEG-7 audio descriptors in practical search environments.
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Tom
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83--91
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Bibliogr. 13 poz., tab., rys.
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- School of Electrical, Computer and Telecommunications Engineering University of Wollongong Wollongong, NSW 2522, Australia
autor
- School of Electrical, Computer and Telecommunications Engineering University of Wollongong Wollongong, NSW 2522, Australia
autor
- School of Electrical, Computer and Telecommunications Engineering University of Wollongong Wollongong, NSW 2522, Australia
autor
- School of Electrical, Computer and Telecommunications Engineering University of Wollongong Wollongong, NSW 2522, Australia
Bibliografia
- [1] ISO/IEC JTC1/SC29/WG11/N4031, “Overview of the MPEG-7 Standard (version 5)”, International Organisation for Standardisation, Singapore, March 2001.
- [2] S. Chang, T. Sikora, and A. Puri, “Overview of the MPEG-7 standard”, IEEE Trans. Circ. Syst. Video Technol., vol. 11, no. 6, pp. 688–695, 2001.
- [3] ISO/IEC FDIS 15938-4, “Information technology multimedia content description interface”, Part 4: “Audio”, International Organisation for Standardisation, Singapore, March 2001.
- [4] M. Casey, “MPEG-7 sound recognition tools”, IEEE Trans. Circ. Syst. Video Technol., vol. 11, no. 6, pp. 737–747, 2001.
- [5] M. Hu and Y. Jian, “MD2L: content description of multimedia documents for efficient process and search/retrieval”, in Proc. IEEE Forum Res. & Technol. Adv. Dig. Libr., 1999, pp. 200–213.
- [6] ISO/IEC JTCI/SC29, “Information technology-coding of motion pictures and associated audio for digital storage media upto about 1.5 Mbit/s – IS 11172”, Part 3: “Audio”, 1992.
- [7] Microsoft, “Windows media encoder”, July 2002, available AT http://www.microsoft.com/windows/windowsmedia/WM7/encoder/whitepaper.asp
- [8] S. Quackenbush and A. Lindsay, “Overview of MPEG-7 audio”, IEEE Trans. Circ. Syst. Video Technol., vol. 11, no. 6, pp. 725–729, 2001.
- [9] E. Allamanche, “Robust matching of audio signals using spectra flatness features”, in IEEE Worksh. Appl. Signal Proc. Audio Acoust., 2001, pp. 127–130.
- [10] M. Yong, G. Davidson, and A. Gersho, “Encoding of LPC spectra parameters using switched-adaptive interframe vector prediction”, In Proc. ICASSP, 1988, vol. 1, pp. 402–405.
- [11] A. Gersho and R. M. Gray, Vector Quantisation and Signal Compression. Kluwer, 1992.
- [12] I. H. Witten and E. Frank, Data Mining Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
- [13] Cubist (version 1.13), Rulequest Research, www.rulequest.com
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Bibliografia
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bwmeta1.element.baztech-article-BPS2-0021-0027