iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-319-12976-1_25
The Large Time-Frequency Analysis Toolbox 2.0 | SpringerLink
Skip to main content

The Large Time-Frequency Analysis Toolbox 2.0

  • Conference paper
  • First Online:
Sound, Music, and Motion (CMMR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8905))

Included in the following conference series:

Abstract

The Large Time Frequency Analysis Toolbox (LTFAT) is a modern Octave/Matlab toolbox for time-frequency analysis, synthesis, coefficient manipulation and visualization. It’s purpose is to serve as a tool for achieving new scientific developments as well as an educational tool. The present paper introduces main features of the second major release of the toolbox which includes: generalizations of the Gabor transform, the wavelets module, the frames framework and the real-time block processing framework.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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 EPUB and 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

Similar content being viewed by others

Notes

  1. 1.

    The toolbox does a zero padding implicitly.

References

  1. Balan, R., Casazza, P., Edidin, D.: On signal reconstruction without phase. Appl. Comput. Harmon. Anal. 20(3), 345–356 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  2. Balazs, P.: Frames and finite dimensionality: frame transformation, classification and algorithms. Appl. Math. Sci. 2(41–44), 2131–2144 (2008)

    MATH  MathSciNet  Google Scholar 

  3. Balazs, P., Dörfler, M., Kowalski, M., Torrésani, B.: Adapted and adaptive linear time-frequency representations: a synthesis point of view. IEEE Signal Process. Mag. (Special Issue: Time-Freq. Anal. Appl.) 30(6), 20–31 (2013)

    Article  Google Scholar 

  4. Balazs, P.: Basic definition and properties of Bessel multipliers. J. Math. Anal. Appl. 325(1), 571–585 (2007). http://dx.doi.org/10.1016/j.jmaa.2006.02.012

    Article  MATH  MathSciNet  Google Scholar 

  5. Balazs, P., Dörfler, M., Jaillet, F., Holighaus, N., Velasco, G.A.: Theory, implementation and applications of nonstationary Gabor frames. J. Comput. Appl. Math. 236(6), 1481–1496 (2011). http://ltfat.sourceforge.net/notes/ltfatnote018.pdf

    Article  MATH  MathSciNet  Google Scholar 

  6. Balazs, P., Feichtinger, H.G., Hampejs, M., Kracher, G.: Double preconditioning for Gabor frames. IEEE Trans. Signal Process. 54(12), 4597–4610 (2006). http://dx.doi.org/10.1109/TSP.2006.882100

    Article  MATH  Google Scholar 

  7. Bayram, I., Selesnick, I.W.: On the dual-tree complex wavelet packet and M-band transforms. IEEE Trans. Signal Process. 56(6), 2298–2310 (2008)

    Article  MathSciNet  Google Scholar 

  8. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009). http://dx.doi.org/10.1137/080716542

    Article  MATH  MathSciNet  Google Scholar 

  9. Bölcskei, H., Hlawatsch, F., Feichtinger, H.G.: Frame-theoretic analysis of oversampled filter banks. IEEE Trans. Signal Process. 46(12), 3256–3268 (2002)

    Article  Google Scholar 

  10. Bultheel, A., Martínez, S.: Computation of the fractional Fourier transform. Appl. Comput. Harmon. Anal. 16(3), 182–202 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  11. Candan, C., Kutay, M.A., Ozaktas, H.M.: The discrete fractional Fourier transform. IEEE Trans. Signal Process. 48(5), 1329–1337 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  12. Cohen, L.: Time-frequency distributions-a review. Proc. IEEE 77(7), 941–981 (1989)

    Article  Google Scholar 

  13. Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57, 1413–1457 (2004)

    Article  MATH  Google Scholar 

  14. Daubechies, I., Han, B., Ron, A., Shen, Z.: Framelets: MRA-based constructions of wavelet frames. Appl. Comput. Harmon. Anal. 14(1), 1–46 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  15. Decorsiere, R., Søndergaard, P.L., Buchholz, J., Dau, T.: Modulation filtering using an optimization approach to spectrogram reconstruction. In: Proceedings of Forum Acusticum 2011. European Acoustics Association (2011)

    Google Scholar 

  16. Feichtinger, H.G., Strohmer, T. (eds.): Gabor Analysis and Algorithms. Birkhäuser, Boston (1998)

    MATH  Google Scholar 

  17. Flandrin, P.: Time-Frequency/Time-Scale Analysis, Wavelet Analysis and its Applications, vol. 10. Academic Press Inc., San Diego (1999). (with a preface by Yves Meyer, Translated from the French by Joachim Stöckler)

    Google Scholar 

  18. Gauthier, J., Duval, L., Pesquet, J.: Optimization of synthesis oversampled complex filter banks. IEEE Trans. Signal Process. 57(10), 3827–3843 (2009)

    Article  MathSciNet  Google Scholar 

  19. Goertzel, G.: An algorithm for the evaluation of finite trigonometric series. Am. Math. Mon. 65(1), 34–35 (1958)

    Article  MATH  MathSciNet  Google Scholar 

  20. Griffin, D., Lim, J.: Signal estimation from modified short-time Fourier transform. IEEE Trans. Acoust. Speech Signal Process. 32(2), 236–243 (1984)

    Article  Google Scholar 

  21. Gröchenig, K.: Foundations of Time-Frequency Analysis. Birkhäuser, Boston (2001)

    Book  MATH  Google Scholar 

  22. Holighaus, N., Dörfler, M., Velasco, G.A., Grill, T.: A framework for invertible, real-time constant-Q transforms. IEEE Trans. Audio Speech Lang. Process. 21(4), 775–785 (2013)

    Article  Google Scholar 

  23. Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets. Time-Frequency Methods and Phase Space, pp. 286–297. Springer, Heidelberg (1989)

    Google Scholar 

  24. Kowalski, M.: Sparse regression using mixed norms. Appl. Comput. Harmon. Anal. 27(3), 303–324 (2009). http://hal.archives-ouvertes.fr/hal-00202904/

    Article  MATH  MathSciNet  Google Scholar 

  25. Kurth, F., Clausen, M.: Filter bank tree and M-band wavelet packet algorithms in audio signal processing. IEEE Trans. Signal Process. 47(2), 549–554 (1999)

    Article  Google Scholar 

  26. Merry, R., Steinbuch, M., van de Molengraft, M.: Wavelet theory and applications, a literature study. DCT 2005.53 (2005). http://alexandria.tue.nl/repository/books/612762.pdf

  27. Necciari, T., Balazs, P., Holighaus, N.: Søndergaard, P.L.: The ERBlet transform: an auditory-based time-frequency representation with perfect reconstruction. In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), pp. 498–502. IEEE, Vancouver, May 2013

    Google Scholar 

  28. Ozaktas, H.M., Zalevsky, Z., Kutay, M.A.: The Fractional Fourier Transform with Applications in Optics and Signal Processing. Wiley, New York (2001)

    Google Scholar 

  29. Perraudin, N., Balazs, P., Sondergaard, P.: A fast Griffin-Lim algorithm. In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1–4, Oct 2013

    Google Scholar 

  30. Perraudin, N., Holighaus, N., Soendergaard, P., Balazs, P.: Gabor dual windows using convex optimization. In: Proceedings of the 10th International Conference on Sampling Theory and Applications (SAMPTA 2013) (2013)

    Google Scholar 

  31. Perraudin, N., Holighaus, N., Søndergaard, P.L., Balazs, P.: Designing Gabor windows using convex optimization (2014). arXiv:1401.6033

  32. Prelcic, N.G., Márquez, O.W., González, S.: Uvi Wave, the ultimate toolbox for wavelet transforms and filter banks. In: Proceedings of the Fourth Bayona Workshop on Intelligent Methods in Signal Processing and Communications, Bayona, Spain, pp. 224–227 (1996)

    Google Scholar 

  33. Průša, Z.: Segmentwise discrete wavelet transform. Ph.D. thesis, Brno University of Technology, Brno (2012)

    Google Scholar 

  34. Rabiner, L., Schafer, R., Rader, C.: The chirp Z-transform algorithm. IEEE Trans. Audio Electroacoust. 17(2), 86–92 (1969)

    Article  Google Scholar 

  35. Selesnick, I.W.: The double density DWT. In: Petrosian, A.A., Meyer, F.G. (eds.) Wavelets in Signal and Image Analysis, pp. 39–66. Springer, Amsterdam (2001)

    Chapter  Google Scholar 

  36. Selesnick, I.W.: The double-density dual-tree DWT. IEEE Trans. Signal Process. 52(5), 1304–1314 (2004)

    Article  MathSciNet  Google Scholar 

  37. Søndergaard, P.L., Torrésani, B., Balazs, P.: The linear time frequency analysis toolbox. Int. J. Wavelets Multiresolut. Anal. Inf. Process. 10(4), 1250032-1–1250032-27 (2012)

    Google Scholar 

  38. Steffen, P., Heller, P., Gopinath, R., Burrus, C.: Theory of regular M-band wavelet bases. IEEE Trans. Signal Process. 41(12), 3497–3511 (1993)

    Article  MATH  Google Scholar 

  39. Stoeva, D.T., Balazs, P.: Invertibility of multipliers. Appl. Comput. Harmon. Anal. 33(2), 292–299 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  40. Stoeva, D.T., Balazs, P.: Canonical forms of unconditionally convergent multipliers. J. Math. Anal. Appl. 399, 252–259 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  41. Sysel, P., Rajmic, P.: Goertzel algorithm generalized to non-integer multiples of fundamental frequency. EURASIP J. Adv. Signal Process. 2012(1), 56 (2012)

    Article  Google Scholar 

  42. Taswell, C.: Near-best basis selection algorithms with non-additive information cost functions. In: Proceedings of the IEEE International Symposium on Time-Frequency and Time-Scale Analysis, pp. 13–16. IEEE Press (1994)

    Google Scholar 

  43. Wickerhauser, M.V.: Lectures on wavelet packet algorithms. In: Lecture notes, INRIA (1992)

    Google Scholar 

  44. Wiesmeyr, C., Holighaus, N., Søndergaard, P.L.: Efficient algorithms for discrete Gabor transforms on a nonseparable lattice. IEEE Trans. Signal Process. 61(20), 5131–5142 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the people that made contributions to the toolbox: Remi Decorsiere, Monika Dörfler, Nina Engelputzeder, Hans Feichtinger, Thomas Hrycak, Florent Jaillet, A.J.E.M. Janssen, Norbert Kaiblinger, Matthieu Kowalski, Ewa Matusiak, Piotr Majdak, Nathanaël Perraudin, Pavel Rajmic, Thomas Strohmer, Bruno Torrésani, Jordy van Velthoven and Tobias Werther.

We would like to express our gratitude towards authors of the Uvi Wave toolbox [32], from which we have taken some wavelet filters generation routines.

The work on this paper was partly supported by the Austrian Science Fund (FWF) START-project FLAME (‘Frames and Linear Operators for Acoustical Modeling and Parameter Estimation’; Y 551-N13).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zdeněk Průša .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Průša, Z., Søndergaard, P.L., Holighaus, N., Wiesmeyr, C., Balazs, P. (2014). The Large Time-Frequency Analysis Toolbox 2.0. In: Aramaki, M., Derrien, O., Kronland-Martinet, R., Ystad, S. (eds) Sound, Music, and Motion. CMMR 2013. Lecture Notes in Computer Science(), vol 8905. Springer, Cham. https://doi.org/10.1007/978-3-319-12976-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12976-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12975-4

  • Online ISBN: 978-3-319-12976-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics