Signal Processing - Special issue on acoustic echo and noise control
Speech Enhancement Using Perceptual Wavelet Packet Decomposition and Teager Energy Operator
Journal of VLSI Signal Processing Systems
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
On time-dependent wavelet denoising
IEEE Transactions on Signal Processing
Two denoising methods by wavelet transform
IEEE Transactions on Signal Processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Wavelet transform domain filters: a spatially selective noise filtration technique
IEEE Transactions on Image Processing
Letters: Variational Bayesian method for speech enhancement
Neurocomputing
Computer Speech and Language
Wavelet-based speech enhancement using time-frequency adaptation
EURASIP Journal on Advances in Signal Processing
Hi-index | 0.00 |
We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to wavelet packet thresholding. This technique does not require an explicit estimation of the noise level or of the a priori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MEL-scale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of signal-to-noise ratio (SNR) is reported for time adaptation and time-scale adaptation of the wavelet coefficients thresholds. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models speech recognition experiments are conducted on the AURORA-2 database and show that the proposed method improves the speech recognition rates for low SNRs.