KHALDI, Kais; BOUDRAA, Abdel-Ouahab; KOMATY, Ali

doi:10.1121/1.4837835

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KHALDI, Kais
Ecole Nationale d'Ingénieurs de Tunis [ENIT]

BOUDRAA, Abdel-Ouahab
Institut de Recherche de l'Ecole Navale [IRENAV]

KOMATY, Ali
Institut de Recherche de l'Ecole Navale [IRENAV]

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Journal of the Acoustical Society of America. 2014-01, vol. 135, n° 1, p. 451-459

Acoustical Society of America

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In this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme isa fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise.< Réduire

Mots clés

Interpolation

Signal de Parole

Opérateur de Teager-Kaiser

Speech

Speech analysis

Wavelets

Décomposition modale empirique

Noise propagation

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Speech enhancement using empirical mode decomposition and the Teager–Kaiser energy operator

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