Skip to main content
SpringerLink
Log in

Intelligent Algorithms for Movie Sound Tracks Restoration

  • Conference paper
Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

Abstract

Two algorithms for movie sound tracks restoration are discussed in the paper. The first algorithm is the unpredictability measure computation applied to the psychoacoustic model-based broadband noise attenuation. A learning decision algorithm, based on a neural network, is employed for determining useful audio signal components acting as maskers of the noisy spectral parts. An application of the rough set decision system to this task is also considered. An iterative method for calculating the sound masking pattern is presented. The second of presented algorithms is the routine for precise evaluation of parasite frequency modulations (wow) utilizing sinusoidal components extracted from the sound spectrum. The results obtained employing proposed intelligent signal processing algorithms, as well as the relationship between both routines, will be presented and discussed in the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Vaseghi, S.: Advanced Signal Processing and Noise Reduction. Wiley & Teubner, New York (1997)

    Google Scholar 

  2. Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical Report of The University of North Carolina in Chapel Hill, USA, No. 95-041

    Google Scholar 

  3. Widrow, B., Stearns, S.: Adaptive Signal Processing. Prentice-Hall Intl. Inc., New Jersey (1985)

    MATH  Google Scholar 

  4. Kunieda, N., Shimamura, T., Suzuki, J., Yashima, H.: Reduction of Noise Level by SPAD (Speech Processing System by Use of Auto-Difference Function). In: International Conference on Spoken Language Processing, Yokohama (1994)

    Google Scholar 

  5. Yoshiya, K., Suzuki, J.: Improvement in Signal-to-Noise Ratio by SPAC (Speech Processing System Using Autocorrelation Function). Electronics and Communications in Japan 61-A(3), 18–24 (1978)

    Google Scholar 

  6. Eprahim, Y.: A Bayesian Estimation for Speech Ebhacement Using Hidden Markov Models. IEEE Transactions on Signal Processing 40(4), 725–735 (1992)

    Article  Google Scholar 

  7. Eprahim, Y.: Statistical-Model-Based Speech Enhacement Systems. Proceedings of the IEEE 80(10), 1526–1555 (1992)

    Article  Google Scholar 

  8. Feder, M., Oppenheim, A., Weinstein, E.: Maximum Likelihood Noise Cancellation Using the EM Algorithm. IEEE Transactions on Acoustics Speech and Signal Processing 37(2), 204–216 (1989)

    Article  Google Scholar 

  9. Lim, J., Oppenheim, A.: Enhancement and Bandwidth Compression of Noisy Speech. Proceedings of the IEEE 67(12), 1586–1604 (1979)

    Article  Google Scholar 

  10. Czyzewski, A., Kaczmarek, A.: Speaker-independent recognition of isolated words using rough sets. In: Proc. Second Annual Joint Conference on Information Sciences, North Carolina, USA, 28 September - 01 October, pp. 397–400 (1995)

    Google Scholar 

  11. Czyzewski, A., Krolikowski, R.: Neuro-Rough Control of Masking Tresholds for Audio Signal Enhancements. Neuro Computing 36(1-4), 5–27 (2001)

    MATH  Google Scholar 

  12. Knecht, W., Schenkel, M., Moschytz, G.: Neural Network Filters for Speech Enhancement. IEEE Transactions on Speech and Audio Processing 3(6), 433–438 (1995)

    Article  Google Scholar 

  13. Asano, F., Hayamizu, S., Yamada, T., Nakamura, S.: Speech Enhacement Based on the Subspace Method. IEEE Transactions on Speech and Audio Processing 8(5), 497–507 (2000)

    Article  Google Scholar 

  14. Elko, G.: Adaptive Noise Cancellation with Directional Microphones. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz (1997)

    Google Scholar 

  15. Wallace, G.: The JPEG: Still Picture Compression Standard. Communication of the ACM 34(4), 31–44 (1991)

    Article  Google Scholar 

  16. Gibson, J., Koo, B.: Filtering of Colored Noise for Speech Enhancement and Coding. IEEE Transactions on Signal Processing 39(8), 1732–1742 (1991)

    Article  Google Scholar 

  17. Lee, K., Jung, S.: Time-Domain Approach Using Multiple Kalman filters and EM Algorithm to Speech Enhacement with Stationary Noise. IEEE Transaction on Signal Processing 44(3), 282–291 (2000)

    Google Scholar 

  18. Ikeda, S., Sugiyama, A.: An Adaptive Noise Canceller with Low Signal Distortion for Speech Codecs. IEEE Transactions on Signal Processing 47(3), 665–674 (1999)

    Article  Google Scholar 

  19. Sambur, M.: Adaptive Noise Cancelling for Speech Signals. IEEE Transactions on Acoustics Speech and Signal Processing ASSP-26(5), 419–423 (1978)

    Article  Google Scholar 

  20. Eprahim, Y., Malah, D., Juang, B.: On the Application of Hidden Markov Models for Enhancing Noisy Speech. IEEE Transactions on Acoustics Speech and Signal Processing 37(12), 1846–1856 (1989)

    Article  Google Scholar 

  21. Sameti, H., Sheikhzadeh, H., Brennan, R.: HMM-Based Strategies for Enhacement of Speech Signals Embeeded in Nonstationary Noise. IEEE Transactions on Speech and Audio Processing 6(5), 445–455 (1998)

    Article  Google Scholar 

  22. Sim, B., Tong, Y., Chang, J., Tan, C.: A Parametric Formulation of the Generalized Spectral Subtraction Method. IEEE Transactions on Speech and Audio Processing 6(4), 328–337 (1998)

    Article  Google Scholar 

  23. Vaseghi, S., Frayling-Cork, R.: Restoration of Old Gramophonic Recordings. Journal of Audio Engineering Society 40(10), 791–800 (1997)

    Google Scholar 

  24. Zwicker, E., Zwicker, T.: Audio Engineering and Psychoacoustics: Matching Signals to the Final Receiver,the Human Auditory System. Journal of Audio Engineering Society 39(3), 115–126 (1991)

    MathSciNet  Google Scholar 

  25. Czyżewski, A., Dziubinski, M.: Noise Reduction in Audio Employing Spectral Unpredictability Measure and Neural Net. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 743–749. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  26. Tsoukalas, D., et al.: Perceptual Filters for Audio Signal Enhacement. Journal of Audio Engineering Society 45(1/2), 22–36 (1997)

    Google Scholar 

  27. MPEG-4, International Standard ISO/IEC FCD 14496-3, Subpart 4 (1998)

    Google Scholar 

  28. Shlien, S.: Guide to MPEG-1 Audio Standard. IEEE Transactions on Broadcasting 40, 206–218 (1994)

    Article  Google Scholar 

  29. Czyzewski, A., Krolikowski, R.: Noise Reduction in Audio Signals Based on the Perceptual Coding Approach. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, pp. 147–150 (October 1999)

    Google Scholar 

  30. Krolikowski, R., Czyzewski, A.: Noise Reduction in Acoustic Signals Using the Perceptual Coding. In: 137th Meeting of Acoustical Society of America, Berlin, CD-Preprint (1998)

    Google Scholar 

  31. McAulay, J., Quatieri, T.F.: Speech Analysis/Synthesis Based on a Sinusoidal Representation. IEEE Transactions on Acoustics, Speech, and Signal Processing 34(4), 744–754 (1986)

    Article  Google Scholar 

  32. Godsill, J.S., Rayner, J.W.: The Restoration of Pitch Variation Defects in Gramophone Recordings. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz (October 1993)

    Google Scholar 

  33. Godsill, J.S.: Recursive Restoration of Pitch Variation Defects in Musical Recordings. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Adelaide, vol. 2, pp. 233–236 (April 1994)

    Google Scholar 

  34. Walmsley, P.J., Godsill, S.J., Rayner, P.J.W.: Polyphonic Pitch Tracking Using Joint Bayesian Estimation of Multiple Frame parameters. In: Proceedings of 1999 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz (October 1999)

    Google Scholar 

  35. Godsill, J.S., Rayner, P.J.W.: Digital Audio Restoration. In: Kahrs, M., Brandenburg, K. (eds.) Applications of Digital Signal Processing to Audio and Acoustics, pp. 41–46. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  36. Godsill, J.S., Rayner, P.J.W.: Digital Audio Restoration - A Statistical Model-Based Approach. Springer, London (1998)

    Google Scholar 

  37. Czyzewski, A., Maziewski, P., Dziubinski, M., Kaczmarek, A., Kostek, B.: Wow Detection and Compensation Employing Spectral Processing of Audio. 117 Audio Engineering Society Convention, Convention Paper 6212, San Francisco (October 2004)

    Google Scholar 

  38. Maziewski, P.: Wow Defect Reduction Based on Interpolation Techniques. In: Proceedings of 4th Polish National Electronic Conference, vol. 1/2, pp. 481–486 (June 2005)

    Google Scholar 

  39. Czyzewski, A., Dziubinski, M., Ciarkowski, A., Kulesza, M., Maziewski, P., Kotus, J.: New Algorithms for Wow and Flutter Detection and Compensation in Audio. 118th Audio Engineering Society Convention, Convention Paper No. 6212, Barcelona (May 2005)

    Google Scholar 

  40. Czyzewski, A., Maziewski, P., Dziubinski, M., Kaczmarek, A., Kulesza, M., Ciarkowski, A.: Methods for Detection and Removal of Parasitic Frequency Modulation in Audio Recordings. In: AES 26th International Conference, Denver (July 2005)

    Google Scholar 

  41. Litwic, L., Maziewski, P.: Evaluation of Wow Defects Based on Tonal Components Detection and Tracking. In: Proceeding of 11th International AES Symposium, Krakow, pp. 145–150 (June 2005)

    Google Scholar 

  42. Czyżewski, A., Dziubinski, M., Litwic, Ł., Maziewski, P.: Intelligent Algorithms for Optical Track Audio Restoration. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 283–293. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  43. Ciarkowski, A., Czyzewski, A., Kulesza, M., Maziewski, P.: DSP Techniques in Wow Defect Evaluation. In: Proceedings of Signal Processing 2005 Workshop, pp. 103–108 (September 2005)

    Google Scholar 

  44. Nichols, J.: An Interactive Pitch Defect Correction System for Archival Audio. In: AES 20th International Conference, Budapest (October 2001)

    Google Scholar 

  45. Howarth, J., Wolfe, P.: Correction of Wow and Flutter Effects in Analog Tape Transfers. 117 Audio Engineering Society Convention, Convention Paper 6213, San Francisco (October 2004)

    Google Scholar 

  46. Wolfe, P., Howarth, J.: Nonuniform Sampling Theory in Audio Signal Processing. 116 Audio Engineering Society Convention, Convention Paper 6123, Berlin (May 2004)

    Google Scholar 

  47. Beerends, J., Stemerdink, J.: A Perceptual Audio Quality Measure Based on a Psychoacoustic Sound Representation. Journal of Audio Engineering Society 40(12), 963–978 (1992)

    Google Scholar 

  48. Humes, L.: Models of the Additivity of Masking. Journal of Acoustical Society of America 85, 1285–1294 (1989)

    Article  Google Scholar 

  49. Brandenburg, K.: Second Generation Perceptual Audio Coding: The Hybrid Coder. In: Proceedings of the 90th Audio Engineering Society Convention, Convetion Paper 2937 Montreux (1990)

    Google Scholar 

  50. Vaseghi, S.: Advanced Signal Processing and Digital Noise Reduction. Wiley&Teubner, New York (1997)

    Google Scholar 

  51. Depalle, P., Garcia, G., Rodet, X.: Analysis of Sound for Additive Synthesis: Tracking of Partials Using Hidden Markov Models. In: Proceedings of IEEE International Conference on Speech and Signal Processing (ICASSP 1993) (1993)

    Google Scholar 

  52. Lagrange, M., Marchand, S., Rault, J.B.: Tracking Partials for Sinusoidal Modeling of Polyphonic Sounds. In: Proceedings of IEEE International Conference on Speech and Signal Processing (ICASSP 2005), Philadelphia (March 2005)

    Google Scholar 

  53. Serra, X.: Musical Sound Modeling with Sinusoids plus Noise. In: Pope, S., Picalli, A., De Poli, G., Roads, C. (eds.) Musical Signal Processing, Swets & Zeitlinger Publishers (1997)

    Google Scholar 

  54. Rodet, X.: Musical Sound Signal Analysis/Synthesis: Sinusoidal + Residual and Elementary Waveform Models. In: Proceedings of IEEE Symposium on Time-Frequency and Time-Scale Analysis (1997)

    Google Scholar 

  55. Lagrange, M., Marchand, S., Rault, J.B.: Sinusoidal Parameter Extraction and Component Selection in a Non-stationary Model. In: Proc. of the 5th Int. Conference on Digital Audio Effects, Hamburg (September 2002)

    Google Scholar 

  56. Auger, F., Flandrin, P.: Improving the Readability of Time-frequency and Time-scale Representations by the Reassignment Method. IEEE Transactions on Signal Processing 43(5), 1068–1089 (1995)

    Article  Google Scholar 

  57. Keiler, F., Marchand, S.: Survey on Extraction of Sinusoids in Stationary Sounds. In: Proceedings of the 5th International Conference on Digital Audio Effects, Hamburg (September 2002)

    Google Scholar 

  58. Sound examples: http://sound.eti.pg.gda.pl/~llitwic/SoundRest/

Download references

Author information

Authors and Affiliations

  1. Multimedia Systems Department, Gdansk University of Technology, ul. Narutowicza 11/12, 80-952, Gdańsk, Poland

    Andrzej Czyżewski, Marek Dziubiński, Łukasz Litwic & Przemysław Maziewski

Authors
  1. Andrzej Czyżewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Dziubiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Łukasz Litwic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Przemysław Maziewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Czyżewski, A., Dziubiński, M., Litwic, Ł., Maziewski, P. (2006). Intelligent Algorithms for Movie Sound Tracks Restoration. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_6

Download citation

  • DOI: https://doi.org/10.1007/11847465_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39382-5

  • Online ISBN: 978-3-540-39383-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation