192 research outputs found

    Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise

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    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

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    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

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    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

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    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

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    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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