Orientadores: João Marcos Travassos Romano, Romis Ribeiro de Faissol Attux / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-12T15:14:54Z (GMT). No. of bitstreams: 1 Dias_TiagoMacedo_M.pdf: 3219855 bytes, checksum: 5572e53d65cb457f420e78b3150dd6ee (MD5) Previous issue date: 2008 / Resumo: Esta dissertação se propõe a estudar um novo método para separação cega de fontes baseado no modelo Post-Nonlinear, que une uma ferramenta de busca global baseada em computação bioinspirada a uma etapa de busca local conduzida pelo algoritmo FastICA. A idéia subjacente à proposta é procurar obter soluções precisas e eficientes usando de maneira parcimoniosa os recursos computacionais disponíveis. A nova proposta foi testada em diferentes cenários, e, em todos os casos, estabeleceram-se comparações com uma abordagem alternativa, cujo passo de otimização não inclui o estágio de busca local (ou "memética"). Os resultados obtidos por meio de simulações indicam que um bom compromisso entre desempenho e custo computacional foi, de fato, atingido. / Resumo: Esta dissertação se propõe a estudar um novo método para separação cega de fontes baseado no modelo Post-Nonlinear, que une uma ferramenta de busca global baseada em computação bioinspirada a uma etapa de busca local conduzida pelo algoritmo FastICA. A idéia subjacente à proposta é procurar obter soluções precisas e eficientes usando de maneira parcimoniosa os recursos computacionais disponíveis. A nova proposta foi testada em diferentes cenários, e, em todos os casos, estabeleceram-se comparações com uma abordagem alternativa, cujo passo de otimização não inclui o estágio de busca local (ou "memética"). Os resultados obtidos por meio de simulações indicam que um bom compromisso entre desempenho e custo computacional foi, de fato, atingido. / Abstract: This work deals with a new method for source separation of Post-Nonlinear mixtures that brings together an evolutionary-based global search and a local search step based on the FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources. The new proposal was tested in different scenarios and, in all cases, we attempted to establish grounds for comparison with an alternative approach whose optimization step does not include the local (memetic) search stage. Simulation results indicate that a good tradeoff between performance and computational cost was indeed reached. / Abstract: This work deals with a new method for source separation of Post-Nonlinear mixtures that brings together an evolutionary-based global search and a local search step based on the FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources. The new proposal was tested in different scenarios and, in all cases, we attempted to establish grounds for comparison with an alternative approach whose optimization step does not include the local (memetic) search stage. Simulation results indicate that a good tradeoff between performance and computational cost was indeed reached. / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Singing voice separation (SVS) can be defined as the process of extracting the vocal element from a given song recording. The impetus for research in this area is mainly that of facilitating certain important applications of music information retrieval (MIR) such as lyrics recognition, singer identification, and melody extraction. To date, the research in the field of SVS has been relatively limited, and mainly focused on the extraction of vocals from monophonic sources. The general approach in this scenario has been one of considering SVS as a blind source separation (BSS) problem. Given the inherent diversity of music, such an approach is motivated by the quest for a generic solution. However, it does not allow the exploitation of prior information, regarding the way in which commercial music is produced. To this end, investigations are conducted into effective methods for unsupervised separation of singing voice from stereophonic studio recordings. The work involves extensive literature review of existing methods that relate to SVS, as well as commercial approaches. Following the identification of shortcomings of the conventional methods, two novel approaches are developed for the purpose of SVS. These approaches, termed SEMANICS and SEMANTICS draw their motivation from statistical as well as spectral properties of the target signal and focus on the separation of voice in the frequency domain. In addition, a third method, named Hybrid SEMANTICS, is introduced that addresses time‐, as well as frequency‐domain separation. As there is lack of a concrete standardised music database that includes a large number of songs, a dataset is created using conventional stereophonic mixing methods. Using this database, and based on widely adopted objective metrics, the effectiveness of the proposed methods has been evaluated through thorough experimental investigations.

This thesis concerns blind source separation techniques using second order statistics and higher order statistics for reverberant environments. A focus of the thesis is algorithmic simplicity with a view to the algorithms being implemented in their online forms. The main challenge of blind source separation applications is to handle reverberant acoustic environments; a further complication is changes in the acoustic environment such as when human speakers physically move. A novel time-domain method which utilises a pair of finite impulse response filters is proposed. The method of principle angles is defined which exploits a singular value decomposition for their design. The pair of filters are implemented within a generalised sidelobe canceller structure, thus the method can be considered as a beamforming method which cancels one source. An adaptive filtering stage is then employed to recover the remaining source, by exploiting the output of the beamforming stage as a noise reference. A common approach to blind source separation is to use methods that use higher order statistics such as independent component analysis. When dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source vector prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. The final algorithm is realised as a real-time embedded application on a floating point Texas Instruments digital signal processor platform. Moving sources, along with reverberant environments, cause significant problems in realistic source separation systems as mixing filters become time variant. A method which employs the pair of cancellation filters, is proposed to cancel one source coupled with an online natural gradient independent vector analysis technique to improve average separation performance in the context of step-wise moving sources. This addresses `dips' in performance when sources move. Results show the average convergence time of the performance parameters is improved. Online methods introduced in thesis are tested using impulse responses measured in reverberant environments, demonstrating their robustness and are shown to perform better than established methods in a variety of situations.

Thanks to its ability to evaluate metabolic functions in tissues from the temporal evolution of a previously injected radiotracer, dynamic positron emission tomography (PET) has become an ubiquitous analysis tool to quantify biological processes. Several quantification techniques from the PET imaging literature require a previous estimation of global time-activity curves (TACs) (herein called \textit{factors}) representing the concentration of tracer in a reference tissue or blood over time. To this end, factor analysis has often appeared as an unsupervised learning solution for the extraction of factors and their respective fractions in each voxel. Inspired by the hyperspectral unmixing literature, this manuscript addresses two main drawbacks of general factor analysis techniques applied to dynamic PET. The first one is the assumption that the elementary response of each tissue to tracer distribution is spatially homogeneous. Even though this homogeneity assumption has proven its effectiveness in several factor analysis studies, it may not always provide a sufficient description of the underlying data, in particular when abnormalities are present. To tackle this limitation, the models herein proposed introduce an additional degree of freedom to the factors related to specific binding. To this end, a spatially-variant perturbation affects a nominal and common TAC representative of the high-uptake tissue. This variation is spatially indexed and constrained with a dictionary that is either previously learned or explicitly modelled with convolutional nonlinearities affecting non-specific binding tissues. The second drawback is related to the noise distribution in PET images. Even though the positron decay process can be described by a Poisson distribution, the actual noise in reconstructed PET images is not expected to be simply described by Poisson or Gaussian distributions. Therefore, we propose to consider a popular and quite general loss function, called the $\beta$-divergence, that is able to generalize conventional loss functions such as the least-square distance, Kullback-Leibler and Itakura-Saito divergences, respectively corresponding to Gaussian, Poisson and Gamma distributions. This loss function is applied to three factor analysis models in order to evaluate its impact on dynamic PET images with different reconstruction characteristics.

Neste trabalho, investigam-se técnicas autodidatas baseadas em estatísticas de ordem superior, aplicadas à equalização de canais de comunicação. Inicialmente, obtém-se um intervalo do passo de adaptação que assegura a convergência do algoritmo do Módulo Constante com o gradiente exato. Algoritmos como o CMA (Constant Modulus Algorithm) e o SWA (Shalvi-Weinstein Algorithm) são revisitados e suas capacidades de tracking analisadas, utilizando-se uma relação de conservação de energia. Além disso, é proposto um algoritmo autodidata denominado AC-CMA (Accelerated Constant Modulus Algorithm) que utiliza a segunda derivada (“aceleração") da estimativa dos coeficientes. Esse algoritmo pode apresentar um compromisso mais favorável entre complexidade computacional e velocidade de convergência que o CMA e o SWA. Esses resultados são estendidos para o caso multiusuário. Através de simulações, os algoritmos são comparados e as análises de convergência e tracking validadas. Considerando o DFE (Decision Feedback Equalizer) no caso monousuário com o critério do módulo constante, é proposto um algoritmo concorrente que evita soluções degeneradas e apresenta um desempenho melhor do que os existentes na literatura. Com o intuito de evitar propagação de erros, é proposta uma estrutura híbrida que utiliza uma rede neural recorrente na malha de realimentação. Resultados de simulações indicam que seu uso pode ser vantajoso para canais lineares e não-lineares. / The equalization of communication channels is addressed by using blind techniques based on higher order statistics. A step-size interval is obtained to ensure the convergence of Steepest-Descent Constant Modulus Algorithm. The Shalvi-Weinstein Algorithm (SWA) and Constant Modulus Algorithm (CMA) are revisited and their tracking capabilities are analyzed by using an energy conservation relation. Moreover, a novel blind algorithm named Accelerated Constant Modulus Algorithm (AC-CMA) is proposed. It adjusts the second derivative (“acceleration") of the coefficient estimates and presents a more favorable compromise between computational complexity and convergence rate than CMA or SWA. These results are extended to the MIMO (Multiple-Input Multiple-Output) case. By means of simulations, the algorithms are compared and the convergence and tracking analysis are validated. The Decision Feedback Equalizer (DFE) is considered in the SISO (Single-Input Single-Output) case with the Constant Modulus criterion and a concurrent algorithm is proposed. It avoids degenerated solutions and shows better behavior than the others presented in the literature. In order to avoid error propagation, a hybrid DFE is also proposed. It includes a recurrent neural network in the feedback filter and may be advantageously used to equalize linear and nonlinear channels.

Neste trabalho, investigam-se técnicas autodidatas baseadas em estatísticas de ordem superior, aplicadas à equalização de canais de comunicação. Inicialmente, obtém-se um intervalo do passo de adaptação que assegura a convergência do algoritmo do Módulo Constante com o gradiente exato. Algoritmos como o CMA (Constant Modulus Algorithm) e o SWA (Shalvi-Weinstein Algorithm) são revisitados e suas capacidades de tracking analisadas, utilizando-se uma relação de conservação de energia. Além disso, é proposto um algoritmo autodidata denominado AC-CMA (Accelerated Constant Modulus Algorithm) que utiliza a segunda derivada (“aceleração”) da estimativa dos coeficientes. Esse algoritmo pode apresentar um compromisso mais favorável entre complexidade computacional e velocidade de convergência que o CMA e o SWA. Esses resultados são estendidos para o caso multiusuário. Através de simulações, os algoritmos são comparados e as análises de convergência e tracking validadas. Considerando o DFE (Decision Feedback Equalizer) no caso monousuário com o critério do módulo constante, é proposto um algoritmo concorrente que evita soluções degeneradas e apresenta um desempenho melhor do que os existentes na literatura. Com o intuito de evitar propagação de erros, é proposta uma estrutura híbrida que utiliza uma rede neural recorrente na malha de realimentação. Resultados de simulações indicam que seu uso pode ser vantajoso para canais lineares e não-lineares. / The equalization of communication channels is addressed by using blind techniques based on higher order statistics. A step-size interval is obtained to ensure the convergence of Steepest-Descent Constant Modulus Algorithm. The Shalvi-Weinstein Algorithm (SWA) and Constant Modulus Algorithm (CMA) are revisited and their tracking capabilities are analyzed by using an energy conservation relation. Moreover, a novel blind algorithm named Accelerated Constant Modulus Algorithm (AC-CMA) is proposed. It adjusts the second derivative (“acceleration”) of the coefficient estimates and presents a more favorable compromise between computational complexity and convergence rate than CMA or SWA. These results are extended to the MIMO (Multiple-Input Multiple-Output) case. By means of simulations, the algorithms are compared and the convergence and tracking analysis are validated. The Decision Feedback Equalizer (DFE) is considered in the SISO (Single-Input Single-Output) case with the Constant Modulus criterion and a concurrent algorithm is proposed. It avoids degenerated solutions and shows better behavior than the others presented in the literature. In order to avoid error propagation, a hybrid DFE is also proposed. It includes a recurrent neural network in the feedback filter and may be advantageously used to equalize linear and nonlinear channels.

Automatic speech recognition has become a standard feature on many consumer electronics and automotive products, and the accuracy of the decoded speech has improved dramatically over time. Often, designers of these products achieve accuracy by employing microphone arrays and beamforming algorithms to reduce interference. However, beamforming microphone arrays are too large for small form factor products such as smart watches. Yet these small form factor products, which have precious little space for tactile user input (i.e. knobs, buttons and touch screens), would benefit immensely from a user interface based on reliably accurate automatic speech recognition. This thesis proposes a solution for interference mitigation that employs blind source separation with a compact array of commercially available unidirectional microphone elements. Such an array provides adequate spatial diversity to enable blind source separation and would easily fit in a smart watch or similar small form factor product. The solution is characterized using publicly available speech audio clips recorded for the purpose of testing automatic speech recognition algorithms. The proposal is modelled in different interference environments and the efficacy of the solution is evaluated. Factors affecting the performance of the solution are identified and their influence quantified. An expectation is presented for the quality of separation as well as the resulting improvement in word error rate that can be achieved from decoding the separated speech estimate versus the mixture obtained from a single unidirectional microphone element. Finally, directions for future work are proposed, which have the potential to improve the performance of the solution thereby making it a commercially viable product.

<p>Magnetic Resonance Imaging is an established technology for both imaging and</p><p>functional studies in clinical and research environments. The field is still very</p><p>research intense. Two major research areas are acquisition time and signal quality.</p><p>The last decade has provided tools for more efficient possibilities of trading these</p><p>factors against each other through parallel imaging.</p><p>In this thesis one parallel imaging method, Sensitivity Encoding for fast</p><p>MRI (SENSE) is examined. An alternative solution CCASENSE is developed.</p><p>CCASENSE reduces the acquisition time by estimating the sensitivity maps required</p><p>for SENSE to work instead of running a reference scan. The estimation</p><p>process is done by Blind Source Separation through Canonical Correlation Analysis.</p><p>It is shown that CCASENSE appears to estimate the sensitivity maps better</p><p>than ICASENSE which is a similar algorithm.</p>

In the recent years, Independent Component Analysis (ICA) has become a fundamental tool in adaptive signal and data processing, especially in the field of Blind Source Separation (BSS). Even though there exist some methods for which an algebraic solution to the ICA problem may be found, other iterative methods are very popular. Among them is the class of information-theoretic approaches, laying on entropies. The associated objective functions are maximized based on optimization schemes, and on gradient-ascent techniques in particular. Two major issues in this field are the following: 1) Does the global maximum point of these entropic objectives correspond to a satisfactory solution of BSS ? and 2) as gradient techniques are used, optimization algorithms look in fact for local maximum points, so what about the meaning of these local optima from the BSS problem point of view? Even though there are some partial answers to these questions in the literature, most of them are based on simulation and conjectures; formal developments are often lacking. This thesis aims at filling this lack and providing intuitive justifications, too. We focus the analysis on Rényi's entropy-based contrast functions. Our results show that, generally speaking, Rényi's entropy is not a suitable contrast function for BSS, even though we recover the well-known results saying that Shannon's entropy-based objectives are contrast functions. We also show that the range-based contrast functions can be built under some conditions on the sources. The BSS problem is stated in the first chapter, and viewed under the information (theory) angle. The two next chapters address specifically the above questions. Finally, the last chapter deals with range-based ICA, the only ``entropy-based contrast' which, based on the enclosed results, is also a <i>discriminant</i> contrast function, in the sense that it is theoretically free of spurious local optima. Geometrical interpretations and surprising examples are given. The interest of this approach is confirmed by testing the algorithm on the MLSP 2006 data analysis competition benchmark; the proposed method outperforms the previously obtained results on large-scale and noisy mixture samples obtained through ill-conditioned mixing matrices.