Speech enhancement using empirical mode decomposition and the Teager-Kaiser energy operator.
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 is a 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.
Ecole Nationale d'Ingénieurs de Tunis, Boite Postale 37, Le Belvédère, 1002 Tunis, Tunisia.
Kais
K
Khaldi
Ecole Navale, CC 600, 29240 Brest Cedex 9, France.
Abdel-Ouahab
AO
Boudraa
Ecole Navale, CC 600, 29240 Brest Cedex 9, France.
Ali
A
Komaty
D006801:Humans; D008962:Models, Theoretical; D009622:Noise; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013018:Sound Spectrography; D013061:Speech Acoustics; D013065:Speech Intelligibility; D013067:Speech Perception; D013068:Speech Production Measurement / Q000379:methods*; D013997:Time Factors; D014833:Voice Quality; D058067:Wavelet Analysis