4,025 research outputs found
Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters
Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions
Comparing and Combining Lexicase Selection and Novelty Search
Lexicase selection and novelty search, two parent selection methods used in
evolutionary computation, emphasize exploring widely in the search space more
than traditional methods such as tournament selection. However, lexicase
selection is not explicitly driven to select for novelty in the population, and
novelty search suffers from lack of direction toward a goal, especially in
unconstrained, highly-dimensional spaces. We combine the strengths of lexicase
selection and novelty search by creating a novelty score for each test case,
and adding those novelty scores to the normal error values used in lexicase
selection. We use this new novelty-lexicase selection to solve automatic
program synthesis problems, and find it significantly outperforms both novelty
search and lexicase selection. Additionally, we find that novelty search has
very little success in the problem domain of program synthesis. We explore the
effects of each of these methods on population diversity and long-term problem
solving performance, and give evidence to support the hypothesis that
novelty-lexicase selection resists converging to local optima better than
lexicase selection
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
Having learned a function from this data, we may want to thereafter integrate
the function into a larger decision-making problem, e.g., for picking the best
chemical process catalyst. We study a large-scale, industrially-relevant
mixed-integer nonlinear nonconvex optimization problem involving both
gradient-boosted trees and penalty functions mitigating risk. This
mixed-integer optimization problem with convex penalty terms broadly applies to
optimizing pre-trained regression tree models. Decision makers may wish to
optimize discrete models to repurpose legacy predictive models, or they may
wish to optimize a discrete model that particularly well-represents a data set.
We develop several heuristic methods to find feasible solutions, and an exact,
branch-and-bound algorithm leveraging structural properties of the
gradient-boosted trees and penalty functions. We computationally test our
methods on concrete mixture design instance and a chemical catalysis industrial
instance
A proximal iteration for deconvolving Poisson noisy images using sparse representations
We propose an image deconvolution algorithm when the data is contaminated by
Poisson noise. The image to restore is assumed to be sparsely represented in a
dictionary of waveforms such as the wavelet or curvelet transforms. Our key
contributions are: First, we handle the Poisson noise properly by using the
Anscombe variance stabilizing transform leading to a {\it non-linear}
degradation equation with additive Gaussian noise. Second, the deconvolution
problem is formulated as the minimization of a convex functional with a
data-fidelity term reflecting the noise properties, and a non-smooth
sparsity-promoting penalties over the image representation coefficients (e.g.
-norm). Third, a fast iterative backward-forward splitting algorithm is
proposed to solve the minimization problem. We derive existence and uniqueness
conditions of the solution, and establish convergence of the iterative
algorithm. Finally, a GCV-based model selection procedure is proposed to
objectively select the regularization parameter. Experimental results are
carried out to show the striking benefits gained from taking into account the
Poisson statistics of the noise. These results also suggest that using
sparse-domain regularization may be tractable in many deconvolution
applications with Poisson noise such as astronomy and microscopy
Voice source characterization for prosodic and spectral manipulation
The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main
components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to
explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection
among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that
the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production
model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its
radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse
filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase.
In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters
reported in the literature, complemented with our own results from the vowel database. The results show that our method gives
satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened
residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system
scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good).
Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first
method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The
second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of
frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in
order to achieve quality levels similar to the reference methods.
As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality
analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to
evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the
original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of
isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in
our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with
previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced
by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good
results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For
each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system
using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of
more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving
the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters
extracted using our algorithm have a positive impact in the field of automatic emotion classification
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