[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

The DCT domain sparsity-assisted detection and recovery of impulsively disturbed samples

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A sparsity-assisted algorithm for the detection and removal of impulsive disturbances is presented. It is assumed that the uncorrupted signal is sparse or highly concentrated in the discrete cosine transform (DCT) domain or the windowed modified DCT (MDCT) domain. To detect whether a specific signal sample is corrupted by a disturbance, the gradient of the sparsity measure is estimated in the space of the signal samples by varying the values of signal samples. Samples whose variations caused the highest estimated gradient values are considered to be far (dislocated) from the sparsity measure minimum. They are labeled as disturbed and further classified as unavailable. Subsequently, these samples are reconstructed using a compressive sensing reconstruction approach. Numerical results support the presented theory and indicate potential applicability in audio signal processing, particularly in the removal of impulsive disturbances and concealment of unavailable, disturbed or lost data.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Adler A, Emiya V, Jafari MG, Elad M, Gribonval R, Plumbley MD (2012) Audio Inpainting. IEEE Transactions on Audio, Speech, and Language Processing 20(3):922–932. https://doi.org/10.1109/TASL.2011.2168211

    Article  Google Scholar 

  2. Avila FR, Biscainho LWP (2012) Bayesian restoration of audio signals degraded by impulsive noise modeled as individual pulses. IEEE Transactions on Audio, Speech, and Language Processing 20(9):2470–2481. https://doi.org/10.1109/TASL.2012.2203811

    Article  Google Scholar 

  3. Baraniuk R (2007) Compressive sensing. IEEE Signal Proc Mag 24 (4):118–121

    Article  MathSciNet  Google Scholar 

  4. Britanak V, Rao KR (2001) An efficient implementation of the forward and inverse MDCT in MPEG audio coding. IEEE Signal Process Lett 8(2):48–51

    Article  Google Scholar 

  5. Buchner H, Skoglund J, Godsill S (2016) An acoustic keystroke transient canceler for speech communication terminals using a semi-blind adaptive filter model. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 614–618

  6. Candès EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30

    Article  Google Scholar 

  7. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52 (4):1289–1306

    Article  MathSciNet  Google Scholar 

  8. Emiya V, Vincent E, Harlander N, Hohmann V (2011) Subjective and objective quality assessment of audio source separation. IEEE Transactions on Audio, Speech and Language Processing 19(7):2046–2057

    Article  Google Scholar 

  9. Godsill SJ, Rayner PJW (1998) Digital audio restoration–a statistical model-based approach. Springer, Berlin

    Book  Google Scholar 

  10. Huber R, Kollmeier B (2006) PEMO-Q–A new method for objective audio quality assessment using a model of auditory perception. IEEE Transactions on Audio, Speech, and Language Processing 14(6):1902–1911

    Article  Google Scholar 

  11. Jokanovic B, Amin MG, Zhang YD (2014) Reducing noise in the time-frequency representation using sparsity promoting kernel design, Proc. SPIE 9109, Compressive Sensing III, 91090B, DOIdoi.org/10.1117/12.2050894

  12. Lee BK, Chang JH (2016) Packet loss concealment based on deep neural networks for digital speech transmission. IEEE/ACM Transactions on Audio, Speech, and Language Processing 24(2):378–387

    Article  Google Scholar 

  13. Stanković L, Brajović M (2018) Analysis of the reconstruction of sparse signals in the DCT domain applied to audio signals. IEEE/ACM Transactions on Audio, Speech, and Language Processing 26(7):1216–1231

    Article  Google Scholar 

  14. Stanković L, Daković M (2016) On a gradient-based algorithm for sparse signal reconstruction in the signal/measurements domain. Math Probl Eng 2016:11. https://doi.org/10.1155/2016/6212674. Article ID 6212674

    Article  MathSciNet  MATH  Google Scholar 

  15. Stanković L, Daković M, Thayaparan T (2013) Time-frequency signal analysis with applications, artech house boston

  16. Stanković L, Daković M, Vujović S (2016) Reconstruction of sparse signals in impulsive disturbance environments. Circuits, Systems and Signal Processing 2016:1–28. https://doi.org/10.1007/s00034-016-0334-3

    Article  MATH  Google Scholar 

  17. Stanković I, Orović I, Daković M, Stanković S (2018) Denoising of Sparse Images in Impulsive Disturbance Environment. Multimedia Tools and Applications 77(5):5885–5905

    Article  Google Scholar 

  18. Volaric I, Sucic V (2016) On the noise impact in the L1 based reconstruction of the sparse time-frequency distributions

  19. Wang JC, Lee YS, Lin CH, Wang SF, Shih CH, Wu CH (2016) Compressive sensing-based speech enhancement. IEEE/ACM Trans on Audio, Speech, and Language Process 24(11):2122–2131

    Article  Google Scholar 

  20. Wu D, Zhu WP, Swamy MNS (2014) The theory of compressive sensing matching pursuit considering time-domain noise with application to speech enhancement. IEEE/ACM Trans on Audio, Speech, and Language Process 22(3):682–696

    Article  Google Scholar 

  21. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEEAccess 3:490–530

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Montenegro, 81000, Podgorica, Montenegro

    Miloš Brajović, Isidora Stanković, Miloš Daković & Ljubiša Stanković

  2. GIPSA Lab, INP Grenoble, 38400, Saint-Martin-d’Hères, France

    Isidora Stanković

Authors
  1. Miloš Brajović
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isidora Stanković
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miloš Daković
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ljubiša Stanković
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miloš Brajović.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brajović, M., Stanković, I., Daković, M. et al. The DCT domain sparsity-assisted detection and recovery of impulsively disturbed samples. Multimed Tools Appl 80, 6221–6234 (2021). https://doi.org/10.1007/s11042-020-09998-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09998-w

Keywords