Circuits, Systems, and Signal Processing

, Volume 37, Issue 8, pp 3412–3440 | Cite as

Detection of the Glottal Closure Instants Using Empirical Mode Decomposition

  • Rajib SharmaEmail author
  • S. R. M. Prasanna
  • Hugo Leonardo Rufiner
  • Gastón Schlotthauer


This work explores the effectiveness of the Intrinsic Mode Functions (IMFs) of the speech signal, in estimating its Glottal Closure Instants (GCIs). The IMFs of the speech signal, which are its AM–FM or oscillatory components, are obtained from two similar nonlinear and non-stationary signal analysis techniques—Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), and Modified Empirical Mode Decomposition (MEMD). Both these techniques are advanced variants of the original technique—Empirical Mode Decomposition (EMD). MEMD is much faster than ICEEMDAN, whereas the latter curtails mode-mixing (a drawback of EMD) more effectively. It is observed that the partial summation of a certain subset of the IMFs results in a signal whose minima are aligned with the GCIs. Based on this observation, two different methods are devised for estimating the GCIs from the IMFs of ICEEMDAN and MEMD. The two methods are captioned ICEEMDAN-based GCIs Estimation (IGE) and MEMD-based GCIs Estimation (MGE). The results reveal that IGE and MGE provide consistent and reliable estimates of the GCIs, compared to the state-of-the-art methods, across different scenarios—clean, noisy, and telephone channel conditions.


Glottal closure instants (GCIs) Empirical mode decomposition (EMD) Improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) Modified empirical mode decomposition (MEMD) Intrinsic mode functions (IMFs) 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Signal Informatics Laboratory, Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Research Institute for Signals, Systems and Computational Intelligence – sinc(i), Facultad de Ingeniería y Ciencias HídricasUniversidad Nacional del LitoralSanta FeArgentina
  3. 3.Laboratorio de Señales y Dinámicas no Lineales, CITER - CONICET, Facultad de IngenieríaUniversidad Nacional de Entre RíosOro VerdeArgentina

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