685 research outputs found
Model-based Speech Enhancement for Intelligibility Improvement in Binaural Hearing Aids
Speech intelligibility is often severely degraded among hearing impaired
individuals in situations such as the cocktail party scenario. The performance
of the current hearing aid technology has been observed to be limited in these
scenarios. In this paper, we propose a binaural speech enhancement framework
that takes into consideration the speech production model. The enhancement
framework proposed here is based on the Kalman filter that allows us to take
the speech production dynamics into account during the enhancement process. The
usage of a Kalman filter requires the estimation of clean speech and noise
short term predictor (STP) parameters, and the clean speech pitch parameters.
In this work, a binaural codebook-based method is proposed for estimating the
STP parameters, and a directional pitch estimator based on the harmonic model
and maximum likelihood principle is used to estimate the pitch parameters. The
proposed method for estimating the STP and pitch parameters jointly uses the
information from left and right ears, leading to a more robust estimation of
the filter parameters. Objective measures such as PESQ and STOI have been used
to evaluate the enhancement framework in different acoustic scenarios
representative of the cocktail party scenario. We have also conducted
subjective listening tests on a set of nine normal hearing subjects, to
evaluate the performance in terms of intelligibility and quality improvement.
The listening tests show that the proposed algorithm, even with access to only
a single channel noisy observation, significantly improves the overall speech
quality, and the speech intelligibility by up to 15%.Comment: after revisio
Implementation and evaluation of a dual-sensor time-adaptive EM algorithm for signal enhancement
Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 1991This thesis describes the implementation and evaluation of an adaptive time-domain algorithm
for signal enhancement from multiple-sensor observations. The algorithm is first
derived as a noncausal time-domain algorithm, then converted into a causal, recursive form.
A more computationally efficient gradient-based parameter estimation step is also presented.
The results of several experiments using synthetic data are shown. These experiments first
illustrate that the algorithm works on data meeting all the assumptions made by the algorithm,
then provide a basis for comparing the performance of the algorithm against the
performance of a noncausal frequency-domain algorithm solving the same problem. Finally,
an evaluation is made of the performance of the simpler gradient-based parameter
estimation step
Sequential Bayesian inference for static parameters in dynamic state space models
A method for sequential Bayesian inference of the static parameters of a
dynamic state space model is proposed. The method is based on the observation
that many dynamic state space models have a relatively small number of static
parameters (or hyper-parameters), so that in principle the posterior can be
computed and stored on a discrete grid of practical size which can be tracked
dynamically. Further to this, this approach is able to use any existing
methodology which computes the filtering and prediction distributions of the
state process. Kalman filter and its extensions to non-linear/non-Gaussian
situations have been used in this paper. This is illustrated using several
applications: linear Gaussian model, Binomial model, stochastic volatility
model and the extremely non-linear univariate non-stationary growth model.
Performance has been compared to both existing on-line method and off-line
methods
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric
distortion on observed sequences that include large moving objects. In order to
provide accurate detail from objects behind the distorting layer, we solve the
space-variant distortion problem using recursive image fusion based on the Dual
Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and
tracked using the improved Gaussian mixture models (GMM) and Kalman filtering.
New fusion rules are introduced which work on the magnitudes and angles of the
DT-CWT coefficients independently to achieve a sharp image and to reduce
atmospheric distortion, respectively. The subjective results show that the
proposed method achieves better video quality than other existing methods with
competitive speed.Comment: IEEE International Conference on Image Processing 201
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