Further Developments of the Online Sound Restoration System for Digital Library Applications

Part of the Studies in Computational Intelligence book series (SCI, volume 541)


New signal processing algorithms were introduced to the online service for audio restoration available at the web address: Missing or distorted audio samples are estimated using a specific implementation of the Jannsen interpolation method. The algorithm is based on the autoregressive model (AR) combined with the iterative complementation of signal samples. Since the interpolation algorithm is computationally complex, an implementation which uses parallel computing has been proposed. Many archival and homemade recordings are at the same time clipped and contain wideband noise. To restore those recordings, the algorithm based on the concatenation of signal clipping reduction and spectral expansion was proposed. The clipping reduction algorithm uses interpolation to replace distorted samples with the estimated ones. Next, spectral expansion is performed in order to reduce the overall level of noise. The online service has been extended also with some copyright protection mechanisms. Certain issues related to the audio copyright problem are discussed with regards to low-level music feature vectors embedded as watermarks. Then, algorithmic issues pertaining watermarking techniques are briefly recalled. The architecture of the designed system along with the employed workflow for embedding and extracting the watermark are described. The implementation phase is presented and the experimental results are reported. The chapter is concluded with a presentation of experimental results of application of described algorithmic extensions to the online sound restoration service.


Automatic audio restoration Noise reduction Impulsive distortions Spectral signal expansion Audio clipping 



The research is supported within the project No. SP/I/1/77065/10 entitled: “Creation of universal, open, repository platform for hosting and communication of networked resources of knowledge for science, education and open society of knowledge”, being a part of Strategic Research Programme “Interdisciplinary system of interactive scientific and technical information” funded by the National Centre for Research and Development (NCBiR, Poland).


  1. 1.
    Czyzewski, A., Kostek, B., Kupryjanow, A.: Online sound restoration for digital library applications. In: Intelligent Tools for Building a Scientific Information Platform Studies in Computational Intelligence, pp. 227–242 (2011)Google Scholar
  2. 2.
    Sony Extended Copy Protection homepage, Accessed 2 May 2013
  3. 3.
    Venkataramu, R.: Analysis and enhancement of apple’s fairplay digital rights management. A Project Report Presented to the Faculty of the Department of Computer Science San Jose State University. In: Partial Fulfillment of the Requirements for the Degree Master of Science, Computer Science (2007)Google Scholar
  4. 4.
    Microsoft Windows Media Digital Rights Management homepage, Accessed 27 May 2013
  5. 5.
    Janus Patent U.S. Patent No. 7,010,808Google Scholar
  6. 6.
    Verance homepage, Accessed 27 May 2013
  7. 7.
    Cinavia homepage, Accessed 27 May 2013
  8. 8.
    AWT (2013) Audio Watermarking Toolkit homepage, Accessed 27 May 2013
  9. 9.
    Coral Consortium homepage, Accessed 27 May 2013
  10. 10.
    Godsill, S., Rayner, P.: Digital Audio Restoration-A Statistical Model-based Approach. Springer, London (1998)Google Scholar
  11. 11.
    Esquef, P., Biscainho, L.: A double-threshold-based approach to impulsive noise detection in audio signals. In: Proceedings of X European Signal Processing Conference (EUSIPCO) (2000)Google Scholar
  12. 12.
    Janssen, A., Veldhuis, R., Vries, L.: Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes. IEEE Trans. Acoust. Speech Signal Process. 34, 317–330 (1986)Google Scholar
  13. 13.
    Dutoit, T., Marqués, F.: Applied Signal Processing: A Matlab-Based Proof of Concept. Springer, Berlin (2009)Google Scholar
  14. 14.
    Cichowski, J., Czyżyk, P., Kostek, B., Czyzewski, A.: Low-level music feature vectors embedded as watermarks. In: Intelligent Tools for Building a Scientific Information Platform Studies in Computational Intelligence. pp. 453–473 (2013)Google Scholar
  15. 15.
    Czyżyk, P., Janusz, C., Czyzewski, A., Bozena, K.: Analysis of impact of lossy audio compression on the robustness of watermark embedded in the DWT domain for non-blind copyright protection. In: Communications in Computer and Information Science, pp. 36–46. Springer, Berlin (2012)Google Scholar
  16. 16.
    Cichowski J., Czyzewski, A., Bozena, K.: Analysis of impact of audio modifications on the robustness of watermark for non-blind architecture. In: Multimedia Tools and Applications, pp. 1–21. Springer, USA (2013)Google Scholar
  17. 17.
    Helen, M., Lahti, T.: Query by example methods for audio signals. In: 7th Nordic Signal Processing Symposium. pp. 302–305 (2006)Google Scholar
  18. 18.
    Lu, L., Saide, F.: Mobile ringtone search through query by humming. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2157–2160 (2008)Google Scholar
  19. 19.
    Plewa, M., Kostek, B.: Creating mood dictionary associated with music. In: 132nd Audio Engineering Society Convention, p. 8607. Budapest, Hungary (2012)Google Scholar
  20. 20.
    RECOMMENDATION ITU-R BS. 1387-1, Method for objective measurements of perceived audio quality. (2001)Google Scholar
  21. 21.
    OPERATM Voice and Audio Quality Analyzer, Accessed 08 April 2013
  22. 22.
    Lang, A., Dittmann, J., Spring, R., Vielhauer, C.: Audio watermark attacks: from single to profile attacks. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 39–50 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Multimedia Systems DepartmentGdansk University of TechnologyGdanskPoland

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