Abstract: We consider the biggest challenge in speech recognition – noise reduction. Traditionally detected transient noise pulses are removed with the corrupted speech using pulse models. In this paper we propose to cope with the problem directly in Dynamic Time Warping domain. Bidirectional Dynamic Time Warping algorithm for the recognition of isolated words impacted by transient noise pulses is proposed. It uses simple transient noise pulse detector, employs bidirectional computation of dynamic time warping and directly manipulates with warping results. Experimental investigation with several alternative solutions confirms effectiveness of the proposed algorithm in the reduction of impact of noise on recognition process – 3.9% increase of the noisy speech recognition is achieved.
Abstract: A new fuzzy filter is presented for noise reduction of
images corrupted with additive noise. The filter consists of two
stages. In the first stage, all the pixels of image are processed for
determining noisy pixels. For this, a fuzzy rule based system
associates a degree to each pixel. The degree of a pixel is a real
number in the range [0,1], which denotes a probability that the pixel
is not considered as a noisy pixel. In the second stage, another fuzzy
rule based system is employed. It uses the output of the previous
fuzzy system to perform fuzzy smoothing by weighting the
contributions of neighboring pixel values. Experimental results are
obtained to show the feasibility of the proposed filter. These results
are also compared to other filters by numerical measure and visual
inspection.
Abstract: Noise contamination in a magnetic resonance (MR)
image could occur during acquisition, storage, and transmission in
which effective filtering is required to avoid repeating the MR
procedure. In this paper, an iterative asymmetrical triangle fuzzy
filter with moving average center (ATMAVi filter) is used to reduce
different levels of salt and pepper noise in a brain MR image. Besides
visual inspection on filtered images, the mean squared error (MSE) is
used as an objective measurement. When compared with the median
filter, simulation results indicate that the ATMAVi filter is effective
especially for filtering a higher level noise (such as noise density =
0.45) using a smaller window size (such as 3x3) when operated
iteratively or using a larger window size (such as 5x5) when operated
non-iteratively.
Abstract: This work presents a fusion of Log Gabor Wavelet
(LGW) and Maximum a Posteriori (MAP) estimator as a speech
enhancement tool for acoustical background noise reduction. The
probability density function (pdf) of the speech spectral amplitude is
approximated by a Generalized Laplacian Distribution (GLD).
Compared to earlier estimators the proposed method estimates the
underlying statistical model more accurately by appropriately
choosing the model parameters of GLD. Experimental results show
that the proposed estimator yields a higher improvement in
Segmental Signal-to-Noise Ratio (S-SNR) and lower Log-Spectral
Distortion (LSD) in two different noisy environments compared to
other estimators.