Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 3/2013

01.09.2013

Wavelet-scalogram based study of non-periodicity in speech signals as a complementary measure of chaotic content

verfasst von: M. Hesham

Erschienen in: International Journal of Speech Technology | Ausgabe 3/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In recent studies, the chaotic behavior of a signal is confirmed using scalogram analysis of continuous-wavelet transform. Chaotic component of a speech signal can be verified through scalogram analysis, since, it investigates the periodicity content of a signal. The periodicity analysis helps in proving that a signal is not periodic, which is an essential condition on chaotic activity. In this work, a scale-index based on scalogram-analysis is calculated for a set of recordings of Arabic vowels. Also, Largest-Lyapunov Exponents (LLE) are computed for these recordings. The obtained measures are, then, compared. The comparison proves the efficacy of scale index for confirming chaotic behavior even for highly-periodic waveforms which is the case in speech vowels. Additionally, it is noted that both LLE and scale-index exhibit classification ability for Arabic vowels.
Vorheriger Artikel Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological Measurement, 26, R155–R199. CrossRef
Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205–211. CrossRef
Benítez, R., Bolós, V. J., & Ramírez, M. E. (2010). A wavelet-based tool for studying non-periodicity. Computers and Mathematics with Applications, 60(3), 634–641. MathSciNetMATHCrossRef
Cantón, E. C., & Murguía, J. S. (2006). Wavelet analysis of chaotic time series. Revista Mexicana de Física, 52(2), 155–162.
Chavan, M. S., Mastorakis, N., Chavan, M. N., & Gaikwad, M. S. (2011). Implementation of SYMLET wavelets to removal of Gaussian additive noise from speech signal. In Proceeding of 10th WSEAS international conference on signal processing, robotics and automation, Wisconsin, USA (pp. 37–41).
Chen, G., Hsu, S., Huang, Y., & Roque-Sol, M. (2011). The spectrum of chaotic time series (II): wavelet analysis. International Journal of Bifurcation and Chaos, 21(5), 457–1467. MATH
Esposito, A., & Marinaro, M. (2005). Some notes on nonlinearities of speech. In Lecture notes in computer science: Vol. 3445. Nonlinear speech modeling and applications (pp. 1–14). Berlin: Springer. CrossRef
Faúndez-Zanuy, M., McLaughlin, S., Esposito, A., Hussain, A., Schoentgen, J., Kubin, G., Kleijn, W. B., & Maragos, P. (2002). Non-linear speech processing: overview and applications. Control and Intelligent Systems, 30(1), 1–10.
Hesham, M. (2006). A predefined wavelet packet for speech quality assessment. Journal of Engineering and Applied Sciences, 53(5), 637–652.
Hou, Y. (2010). A compactly supported, symmetrical and quasi-orthogonal wavelet. International Journal of Wavelets, Multiresolution and Information Processing, 8(6), 931–940. MathSciNetMATHCrossRef
Jiang, J. J., Zhang, Y., & McGilligan, C. (2006). Chaos in voice, from modeling to measurement. Journal of Voice, 20(1), 2–17. CrossRef
Kia, B., Ditto, W. L., & Spano, M. L. (2011). Chaos for speech coding and production. In C. M. Travieso-González & J. Hernández (Eds.), NOLISP (pp. 270–278). Berlin: Springer.
Kokkinos, I., & Maragos, P. (2006). Nonlinear speech analysis using models for chaotic systems. IEEE Transactions on Speech and Audio Processing, 13(6), 1098–1109. CrossRef
Lei, M., Wang, Z., & Feng, Z. (2002). The application of symplectic geometry on nonlinear dynamics analysis of the experimental data. In DSP 2002 (Vol. 2, pp. 1137–1140).
Lin, J., Huang, Z., Wang, Y., & Zhenken, S. (2000). Selection of proper embedding dimension in phase space reconstruction of speech signals. J. of Electron., 17(2), 161–169.
Liu, H. F., & Dai, Z. H. (2005). Noise robust estimates of the largest Lyapunov exponent. Physics Letters A, 341(1–4), 119–127. MATHCrossRef
Liu, X., Povinelli, R., & Johnson, M. (2003). Vowel classification by global dynamic modeling. In ISCA tutorial and research workshop on non-linear speech processing (NOLISP), Le Croisic, France.
Long, Y., Gang, L., & Jun, G. (2004). Selection of the best wavelet base for speech signal. In Proceedings of 2004 international symposium onIntelligent multimedia, video and speech processing, 20–22 Oct. (pp. 218–221). CrossRef
Mallat, S. (1999). A wavelet tour of signal processing. London: Academic Press. MATH
Maragos, P., Dimakis, A. G., & Kokkinos, I. (2002). Some advances in nonlinear speech modeling using modulations, fractals, and chaos. In DSP, 2002 (Vol. 1, pp. 325–332).
Ouahabi, A., & Femmam, S. (2011). Wavelet-based multifractal analysis of 1-D and 2-D signals: new results. Analog Integrated Circuits and Signal Processing, 69(1), 3–15. CrossRef
Palus, M., & Dvorak, I. (1992). Singular-value decomposition in attractor reconstruction: pitfalls and precautions. Physica D, 55(1–2), 221–234. MathSciNetMATHCrossRef
Pitsikalis, V., & Maragos, P. (2002). Speech analysis and feature extraction using chaotic models. In ICASSP 2002 (pp. 533–536).
Pitsikalis, V., & Maragos, P. (2006). Filtered dynamics and fractal dimensions for noisy speech recognition. IEEE Signal Processing Letters, 13(11), 711–714. CrossRef
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D, 65(1–2), 117–134. MathSciNetMATHCrossRef
Vaziri, G., Almasganj, F., & Behroozmand, R. (2010). Pathological assessment of patients’speech signals using nonlinear dynamical analysis. Computers in Biology and Medicine, 40, 54–63. CrossRef
Watada, J., & Matsumoto, Y. (2007). Wavelet approach to chaotic forecasting of stock movement. Asia Pacific Journal of Finance and Banking Research, 1(1), 34–44.
Wickerhauser, M. V. (1994). Adapted wavelet analysis from theory to software. New York: IEEE Press. MATH
Metadaten
Titel
Wavelet-scalogram based study of non-periodicity in speech signals as a complementary measure of chaotic content
verfasst von
M. Hesham
Publikationsdatum
01.09.2013
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 3/2013
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-013-9187-3

Weitere Artikel der Ausgabe 3/2013

International Journal of Speech Technology 3/2013 Zur Ausgabe

OriginalPaper

Recognizing the message and the messenger: biomimetic spectral analysis for robust speech and speaker recognition

OriginalPaper

VoCMex: a voice corpus in Mexican Spanish for research in speaker recognition

OriginalPaper

MCRA noise estimation for KLT-VRE-based speech enhancement

OriginalPaper

Advanced classification approach for neuronal phoneme recognition system based on efficient constructive training algorithm

OriginalPaper

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

OriginalPaper

Implementation of variable bitrate data hiding techniques on standard and proposed GSM 06.10 full rate coder and its overall comparative evaluation of performance

Neuer Inhalt

    Bildnachweise