Publication Type:

Journal Article

Source:

Circuits, Systems, and Signal Processing, Volume 37, Number 2, p.810–830 (2018)

URL:

https://doi.org/10.1007/s00034-017-0582-x

Abstract:

The objective of the proposed work is to accurately estimate the glottal closure instants (GCIs) and glottal opening instant (GOIs) from electroglottographic (EGG) signals. This work also addresses the issues with existing EGG-based GCI/GOI detection methods. GCIs are the instants at which excitation to the vocal tract is maximum and GOIs, on the other hand, have minimum excitation compared to GCIs. Both these instants occur instantaneously with a fundamental frequency defined for each glottal cycle in a given EGG signal. Accurate detection of these instants from the EGG signal is essential for the performance evaluation of GCIs and GOIs estimated from the speech signal directly. This work proposes a new method for accurate detection of GCIs and GOIs from the EGG signal using variational mode decomposition (VMD) algorithm. The EGG signal has been decomposed into sub-signals using the VMD algorithm. It is shown that VMD captures the center frequency close to the fundamental frequency of the EGG signal through one of its modes. This property of the corresponding mode helps to estimate GCIs and GOIs from the same. Besides, instantaneous pitch frequency is estimated from the obtained GCIs. The proposed method has been evaluated on the CMU-arctic database for GCI/GOI estimation and the Keele pitch extraction reference database for instantaneous pitch frequency estimation. The effectiveness of the proposed method is confirmed by comparison with state-of-the-art methods. Experimental results show that the proposed method has better accuracy and identification rate compared to state-of-the-art methods.

Notes:

cited By 0

Cite this Research Publication

J. G. Lal, Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 810–830, 2018.

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