2 research outputs found
Modeling of Teager Energy Operated Perceptual Wavelet Packet Coefficients with an Erlang-2 PDF for Real Time Enhancement of Noisy Speech
In this paper, for real time enhancement of noisy speech, a method of
threshold determination based on modeling of Teager energy (TE) operated
perceptual wavelet packet (PWP) coefficients of the noisy speech and noise by
an Erlang-2 PDF is presented. The proposed method is computationally much
faster than the existing wavelet packet based thresholding methods. A custom
thresholding function based on a combination of mu-law and semisoft
thresholding functions is designed and exploited to apply the statistically
derived threshold upon the PWP coefficients. The proposed custom thresholding
function works as a mu-law or a semisoft thresholding function or their
combination based on the probability of speech presence and absence in a
subband of the PWP transformed noisy speech. By using the speech files
available in NOIZEUS database, a number of simulations are performed to
evaluate the performance of the proposed method for speech signals in the
presence of Gaussian white and street noises. The proposed method outperforms
some of the state-of-the-art speech enhancement methods both at high and low
levels of SNRs in terms of standard objective measures and subjective
evaluations including formal listening tests.Comment: To appear in Digital Signal Processing, 27 pages, 19 figures, 10
table
On reconstruction algorithms for signals sparse in Hermite and Fourier domains
This thesis consists of original contributions in the area of digital signal
processing. The reconstruction of signals sparse (highly concentrated) in
various transform domains is the primary problem analyzed in the thesis. The
considered domains include Fourier, discrete Hermite, one-dimensional and
two-dimensional discrete cosine transform, as well as various time-frequency
representations. Sparse signals are reconstructed using sparsity measures,
being, in fact, the measures of signal concentration in the considered domains.
The thesis analyzes the compressive sensing reconstruction algorithms and
introduces new approaches to the problem at hand. The missing samples influence
on analyzed transform domains is studied in detail, establishing the relations
with the general compressive sensing theory. This study provides new insights
on phenomena arising due to the reduced number of signal samples. The
theoretical contributions involve new exact mathematical expressions which
describe performance and outcomes of reconstruction algorithms, also including
the study of the influence of additive noise, sparsity level and the number of
available measurements on the reconstruction performance, exact expressions for
reconstruction errors and error probabilities. Parameter optimization of the
discrete Hermite transform is also studied, as well as the additive noise
influence on Hermite coefficients, resulting in new parameter optimization and
denoising algorithms. Additionally, an algorithm for the decomposition of
multivariate multicomponent signals is introduced, as well as an instantaneous
frequency estimation algorithm based on the Wigner distribution. Extensive
numerical examples and experiments with real and synthetic data validate the
presented theory and shed a new light on practical applications of the results.Comment: Ph.D. thesis, 241 pages, in Montenegrin/Serbia