Music Rhythm Customized Mobile Application Based on Information Extraction

  • Yining LiEmail author
  • Wei Hu
  • Yonghao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)


Information extraction technology to be able to measure, store, collect all kinds of information, especially the direct access to important information, which is based on mobile applications and more convenient for the information gathering process, user and information feedback, greatly reduce the cost of information technology, makes the implementation of large-scale information extraction technology possible. As for this paper, first of all, mainly introduces the basic theory of music rhythm customization mobile application; Secondly, the development and implementation of this application are introduced; Finally, it summarizes and anticipates the future development trend of music rhythm customization technology. After implementation, users only need to import music or video that they want to modify, select the corresponding style or double speed, and then get relevant audio results through system software processing. And make music rhythm customization easy to operate, which remove a lot of irrelevant operations, so that users do not need to know the relevant professional knowledge can be processed.


Rhythm tracking Audio processing Information extraction A mobile application 


  1. 1.
    Yu, Z.: The positioning, reasons and significance of the relationship between rites and music in early confucianism. Film Rev. Introduction 18, 106–109 (2010)Google Scholar
  2. 2.
    Ravi, N.D., Bhalke, D.G.: Musical instrument information retrieval using neural network (2016)Google Scholar
  3. 3.
    Wang, Y.: Concept and practice of data journalism in the context of big data. Mod. Media 23(6), 16–17 (2015)Google Scholar
  4. 4.
    Fu, H., Chen, C., Xiang, Y., et al.: Research and implementation of key technologies for distributed big data acquisition. Guangdong Commun. Technol. 35(10), 7–10 (2015)Google Scholar
  5. 5.
    Davies, M.E.P., Plumbley, M.D.: Context-dependent beat tracking of musical audio. IEEE Trans. Audio Speech Lang. Process. 15(3), 009–1020 (2007)CrossRefGoogle Scholar
  6. 6.
    Chu, W., Champagne, B.F.: Further studies of a FFT-based auditory spectrum with application in audio classification. In: International Conference on Signal Processing, pp. 1–3 (2008)Google Scholar
  7. 7.
    Degara, N., Rua, E.A., Pena, A., et al.: Reliability-informed beat tracking of musical signals. IEEE Trans. Audio Speech Lang. Process. 20(1), 290–301 (2012)CrossRefGoogle Scholar
  8. 8.
    Mohapatra, B.N., Mohapatra, R.K.: FFT and sparse FFT techniques and applications. In: Fourteenth International Conference on Wireless & Optical Communications Networks, pp. 1–5. IEEE (2017)Google Scholar
  9. 9.
    Zhan, Y., Yuan, X.: Audio post-processing detection and identification based on audio features. In: International Conference on Wavelet Analysis & Pattern Recognition, pp. 3–7. IEEE (2017)Google Scholar
  10. 10.
    Greamo, C., Ghosh, A.: Sandboxing and virtualization: modern tools for combating malware. IEEE Secur. Priv. 9(2), 79–82 (2011)CrossRefGoogle Scholar
  11. 11.
    Pang, B.: Development trend of interface design from flat style. Decoration 4, 127–128 (2014)Google Scholar
  12. 12.
    Roig, C., Tardón, L.J., Barbancho, I., et al.: Automatic melody composition based on a probabilistic model of music style and harmonic rules. Knowl.-Based Syst. 71, 419–434 (2014)CrossRefGoogle Scholar
  13. 13.
    Marchand, S.: Fourier-based methods for the spectral analysis of musical sounds. In: Signal Processing Conference, pp. 1–5. IEEE (2014)Google Scholar
  14. 14.
    Li, P., Zou, Z.: Loop of the scroll view class (UIScrollView) in apple iOS and algorithm of dynamic image loading. Comput. Telecom 10, 54–55 (2011)Google Scholar
  15. 15.
    Liu, C., Zhou, B., Guo, S.: Load optimization of large quantity data based on UITableView in iOS. J. Hangzhou Univ. Electron. Sci. Technol. 4, 46–49 (2013)Google Scholar
  16. 16.
    Ma, Z.: Digital rights management: model, technology and application. China Commun. 14(6), 156–167 (2017)CrossRefGoogle Scholar
  17. 17.
    Kim, B., Pardo, B.: Speeding learning of personalized audio equalization. In: International Conference on Machine Learning & Applications, 3–6 (2015)Google Scholar
  18. 18.
    Rong, F.: Audio classification method based on machine learning. In: International Conference on Intelligent Transportation, pp. 3–5. IEEE Computer Society (2016)Google Scholar
  19. 19.
    Liu, L., Bian, J., Zhang, L., et al.: Implementation of audio and video synchronization based on FFMPEG decoding. Comput. Eng. Des. 34(6), 2087–2092 (2013)Google Scholar
  20. 20.
    Akram, F., Garcia, M.A., Puig, D.: Active contours driven by local and global fitted image models for image segmentation robust to intensity in homogeneity. PLoS ONE 12(4), 1–32 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industria SystemWuhanChina
  3. 3.Digital Media LabBirmingham City UniversityBirminghamUK

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