192 research outputs found
Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
We consider the problem of simultaneous reduction of acoustic echo,
reverberation and noise. In real scenarios, these distortion sources may occur
simultaneously and reducing them implies combining the corresponding
distortion-specific filters. As these filters interact with each other, they
must be jointly optimized. We propose to model the target and residual signals
after linear echo cancellation and dereverberation using a multichannel
Gaussian modeling framework and to jointly represent their spectra by means of
a neural network. We develop an iterative block-coordinate ascent algorithm to
update all the filters. We evaluate our system on real recordings of acoustic
echo, reverberation and noise acquired with a smart speaker in various
situations. The proposed approach outperforms in terms of overall distortion a
cascade of the individual approaches and a joint reduction approach which does
not rely on a spectral model of the target and residual signals
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding
A fundamental question in designing lossy data compression schemes is how
well one can do in comparison with the rate-distortion function, which
describes the known theoretical limits of lossy compression. Motivated by the
empirical success of deep neural network (DNN) compressors on large, real-world
data, we investigate methods to estimate the rate-distortion function on such
data, which would allow comparison of DNN compressors with optimality. While
one could use the empirical distribution of the data and apply the
Blahut-Arimoto algorithm, this approach presents several computational
challenges and inaccuracies when the datasets are large and high-dimensional,
such as the case of modern image datasets. Instead, we re-formulate the
rate-distortion objective, and solve the resulting functional optimization
problem using neural networks. We apply the resulting rate-distortion
estimator, called NERD, on popular image datasets, and provide evidence that
NERD can accurately estimate the rate-distortion function. Using our estimate,
we show that the rate-distortion achievable by DNN compressors are within
several bits of the rate-distortion function for real-world datasets.
Additionally, NERD provides access to the rate-distortion achieving channel, as
well as samples from its output marginal. Therefore, using recent results in
reverse channel coding, we describe how NERD can be used to construct an
operational one-shot lossy compression scheme with guarantees on the achievable
rate and distortion. Experimental results demonstrate competitive performance
with DNN compressors
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
On pattern recognition of brain connectivity in resting-state functional MRI
Dissertação de mestrado integrado em Biomedical Engineering (specialization on Medical Informatics)The human urge and pursuit for information have led to the development of increasingly complex
technologies, and new means to study and understand the most advanced and intricate biological
system: the human brain. Large-scale neuronal communication within the brain, and how it relates to
human behaviour can be inferred by delving into the brain network, and searching for patterns in
connectivity. Functional connectivity is a steady characteristic of the brain, and it has been proved to be
very useful for examining how mental disorders affect connections within the brain. The detection of
abnormal behaviour in brain networks is performed by experts, such as physicians, who limit the process
with human subjectivity, and unwittingly introduce errors in the interpretation. The continuous search for
alternatives to obtain faster and robuster results have put Machine Learning and Deep Learning in the
leading position of computer vision, as they enable the extraction of meaningful patterns, some beyond
human perception.
The aim of this dissertation is to design and develop an experiment setup to analyse functional
connectivity at a voxel level, in order to find functional patterns. For the purpose, a pipeline was outlined
to include steps from data download to data analysis, resulting in four methods: Data Download, Data
Preprocessing, Dimensionality Reduction, and Analysis. The proposed experiment setup was modeled
using as materials resting state fMRI data from two sources: Life and Health Sciences Research Institute
(Portugal), and Human Connectome Project (USA). To evaluate its performance, a case study was
performed using the In-House data for concerning a smaller number of subjects to study. The pipeline
was successful at delivering results, although limitations concerning the memory of the machine used
restricted some aspects of this experiment setup’s testing.
With appropriate resources, this experiment setup may support the process of analysing and extracting
patterns from any resting state functional connectivity data, and aid in the detection of mental disorders.O desejo e a busca intensos do ser humano por informação levaram ao desenvolvimento de
tecnologias cada vez mais complexas e novos meios para estudar e entender o sistema biológico mais
avançado e intrincado: o cérebro humano. A comunicação neuronal em larga escala no cérebro, e como
ela se relaciona com o comportamento humano, pode ser inferida investigando a rede neuronal cerebral
e procurando por padrões de conectividade. A conectividade funcional é uma característica constante do
cérebro e provou ser muito útil para examinar como os distúrbios mentais afetam as conexões cerebrais.
A deteção de anormalidades em imagens de ressonância magnética é realizada por especialistas, como
médicos, que limitam o processo com a subjetividade humana e, inadvertidamente, introduzem erros na
interpretação. A busca contínua de alternativas para obter resultados mais rápidos e robustos colocou
as técnicas de machine learning e deep learning na posição de liderança de visão computacional, pois
permitem a extração de padrões significativos e alguns deles para além da percepção humana.
O objetivo desta dissertação é projetar e desenvolver uma configuração experimental para analisar
a conectividade funcional ao nível do voxel, a fim de encontrar padrões funcionais. Nesse sentido, foi
delineado um pipeline para incluir etapas a começar no download de dados até à análise desses mesmos
dados, resultando assim em quatro métodos: Download de Dados, Pré-processamento de Dados, Redução
de Dimensionalidade e Análise. A configuração experimental proposta foi modelada usando dados de
ressonância magnética funcional de resting-state de duas fontes: Instituto de Ciências da Vida e Saúde
(Portugal) e Human Connectome Project (EUA). Para avaliar o seu desempenho, foi realizado um estudo de
caso usando os dados internos por considerar um número menor de participantes a serem estudados.
O pipeline foi bem-sucedido em fornecer resultados, embora limitações relacionadas com a memória da
máquina usada tenham restringido alguns aspetos do teste desta configuração experimental.
Com recursos apropriados, esta configuração experimental poderá servir de suporte para o processo
de análise e extração de padrões de qualquer conjunto de dados de conectividade funcional em resting-state
e auxiliar na deteção de transtornos mentais
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
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