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RETRACTED ARTICLE: Research on the Method of Music Content Analysis Based on Fuzzy Classification

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This article was retracted on 13 December 2022

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Abstract

In order to analyze the methods of music content research, this paper systematically introduces the fuzzy classification, then constructs the basic framework of music content authentication based on fuzzy classification, in which the content of music with Chroma features were extracted. At the same time, the robust Hash is calculated to verify the accuracy of algorithm to distinguish ability to maintain content operation and malicious tampering and tamper localization. The authentication algorithm of the music signal is divided into a series of unequal note segments, so that each certification unit contains the complete semantic information, and effectively solve the problem of lost synchronization in time domain algorithms exist, for each note fragment extraction contains rich information of the characteristics of middle melody. On this basis, the robust values were calculated, and the metrics were defined based on the statistical and time distribution characteristics that differ from the original music values. In this experiment, the fuzzy logic method was used to classify and made the authentication decision.

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References

  1. Preissler, P., Kordovan, S., Ullrich, A., et al. (2016). Favored subjects and psychosocial needs in music therapy in terminally ill cancer patients: A content analysis. BMC Palliative Care, 15(1), 48.

    Article  Google Scholar 

  2. Brooks, W. L. (2015). Music in infant-directed digital video discs: A content analysis. Music Education Research, 17(2), 141–161.

    Article  Google Scholar 

  3. Baveye, Y., Dellandrea, E., Chamaret, C., et al. (2015). Liris-accede: A video database for affective content analysis. IEEE Transactions on Affective Computing, 6(1), 43–55.

    Article  Google Scholar 

  4. Tsiris, G., Spiro, N., Pavlicevic, M., et al. (2014). What does the past tell us? A content analysis of the first quarter-century of the British Journal of Music Therapy. British Journal of Music Therapy, 28(1), 4–24.

    Article  Google Scholar 

  5. Melin, P., Olivas, F., Castillo, O., et al. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications, 40(8), 3196–3206.

    Article  Google Scholar 

  6. Lin, Y. H., & Tsai, M. S. (2014). Non-intrusive load monitoring by novel neuro-fuzzy classification considering uncertainties. IEEE Transactions on Smart Grid, 5(5), 2376–2384.

    Article  Google Scholar 

  7. Karagiannis, A., Cauli, B., Battaglia, D., et al. (2013). Beyond the frontiers of neuronal types: fuzzy classification of interneurons. BMC neuroscience, 14(1), 56.

    Google Scholar 

  8. Luo, F., Dong, Z., Chen, G., et al. (2015). Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment. IEEE Transactions on Industrial Informatics, 11(2), 416–426.

    Article  Google Scholar 

  9. Jiménez, F., Sánchez, G., Juárez, J. M., et al. (2014). Multi-objective evolutionary algorithms for fuzzy classification in survival prediction. Artificial Intelligence in Medicine, 60(3), 197–219.

    Article  Google Scholar 

  10. Mastropietro, D. J., & Omidian, H. (2015). Abuse-deterrent formulations: Part 1-development of a formulation-based classification system. Expert opinion on drug metabolism & toxicology, 11(2), 193–204.

    Article  Google Scholar 

  11. Sowmya, K. N., & Chennamma, H. R. (2015). A survey on video forgery detection. International Journal of Computer Engineering and Applications, 9(2), 17–27.

    Google Scholar 

  12. Rigoni, R., Freitas, P. G., Farias, M. C., et al. (2016). Detecting tampering in audio-visual content using QIM watermarking. Information Sciences, 328(9), 127–143.

    Article  Google Scholar 

  13. Xiao, S., & Xu, Z. (2017). Reliable and energy efficient communication algorithm in hierarchical wireless sensor networks. Wireless Personal Communications, 95(3), 1891–1909.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by general project of research on philosophy and social science of colleges and universities in Jiangsu (2016SJD760060) .

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Correspondence to Zhen Li.

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Li, Z., Gao, X. RETRACTED ARTICLE: Research on the Method of Music Content Analysis Based on Fuzzy Classification. Wireless Pers Commun 102, 1949–1962 (2018). https://doi.org/10.1007/s11277-018-5248-x

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  • DOI: https://doi.org/10.1007/s11277-018-5248-x

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