Dissertations / Theses on the topic 'Blind Source Separation (BSS)'
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Vikram, Anil Babu. "Tracking in wireless sensor network using blind source separation algorithms." Cleveland, Ohio : Cleveland State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=csu1259959597.
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Thesis (M.S.)--Cleveland State University, 2009.Abstract. Title from PDF t.p. (viewed on Dec. 2, 2009). Includes bibliographical references (p. 65-72). Available online via the OhioLINK ETD Center and also available in print.
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Ziehe, Andreas. "Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing." Phd thesis, [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976710331.
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Marin, Jorge I. "Robust binaural noise-reduction strategies with binaural-hearing-aid constraints: design, analysis and practical considerations." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44747.
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The objective of the dissertation research is to investigate noise reduction methods for binaural hearing aids based on array and statistical signal processing and inspired by a human auditory model. In digital hearing aids, wide dynamic range compression (WDRC) is the most successful technique to deal with monaural hearing losses. This WDRC processing is usually performed after a monaural noise reduction algorithm. When hearing losses are present in both ears, i.e., a binaural hearing loss, independent monaural hearing aids have been shown not to be comfortable for most users, preferring a processing that involves synchronization between both hearing devices. In addition, psycho-acoustical studies have identified that under hostile environments, e.g., babble noise at very low SNR conditions, users prefer to use linear amplification rather than WDRC. In this sense, the noise reduction algorithm becomes an important component of a digital hearing aid to provide improvement in speech intelligibility and user comfort. Including a wireless link between both hearing aids offers new ways to implement more efficient methods to reduce the background noise and coordinate processing for the two ears. This approach, called binaural hearing aid, has been recently introduced in some commercial products but using very simple processing strategies. This research analyzes the existing binaural noise-reduction techniques, proposes novel perceptually-inspired methods based on blind source separation (BSS) and multichannel Wiener filter (MWF), and identifies different strategies for the real-time implementation of these methods. The proposed methods perform efficient spatial filtering, improve SNR and speech intelligibility, minimize block processing artifacts, and can be implemented in low-power architectures.
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Naik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.
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This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: The iterative nature of ICA The order and magnitude ambiguity problems of ICA Estimation of number of sources based on dependency and independency nature of the signals Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.
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Laruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.
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L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN)The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)
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Toumi, Ichrak. "Decomposition methods of NMR signal of complex mixtures : models ans applications." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4351/document.
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L'objectif de ce travail était de tester des méthodes de SAS pour la séparation des spectres complexes RMN de mélanges dans les plus simples des composés purs. Dans une première partie, les méthodes à savoir JADE et NNSC ont été appliqué es dans le cadre de la DOSY , une application aux données CPMG était démontrée. Dans une deuxième partie, on s'est concentré sur le développement d'un algorithme efficace "beta-SNMF" . Ceci s'est montré plus performant que NNSC pour beta inférieure ou égale à 2. Etant donné que dans la littérature, le choix de beta a été adapté aux hypothèses statistiques sur le bruit additif, une étude statistique du bruit RMN de la DOSY a été faite pour obtenir une image plus complète de nos données RMN étudiéesThe objective of the work was to test BSS methods for the separation of the complex NMR spectra of mixtures into the simpler ones of the pure compounds. In a first part, known methods namely JADE and NNSC were applied in conjunction for DOSY , performing applications for CPMG were demonstrated. In a second part, we focused on developing an effective algorithm "beta- SNMF ". This was demonstrated to outperform NNSC for beta less or equal to 2. Since in the literature, the choice of beta has been adapted to the statistical assumptions on the additive noise, a statistical study of NMR DOSY noise was done to get a more complete picture about our studied NMR data
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Korczowski, Louis. "Méthodes pour l'électroencéphalographie multi-sujet et application aux interfaces cerveau-ordinateur." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT078/document.
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L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classification de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG). Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle est pourtant difficile quand les signaux EEG concernés sont transitoires avec une grande variabilité (intra- et inter-sujet) spatio-temporelle et avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existantsThe study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. This dissertation includes three interdependent contribution. The first contribution is the development of an experimental platform consisting of a multi-player video game, namely Brain Invaders 2, controlled by classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry. Those approaches are theoretically and experimentally compared during the second contribution ; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain during the second contribution. Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with an great spatio-temporal variability (intra- and inter-subject) and with low signal-to-noise ratio such as ERP. Therefore, as third contribution, we propose a new blind source separation model, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources that allows to compensate this variability. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrate on Brain Invaders 2 data that our solution extracts simultaneously evoked and ongoing EEG sources and performs better in term of accuracy and robustness compared to the existing models
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Boulais, Axel. "Méthodes de séparation aveugle de sources et application à l'imagerie hyperspectrale en astrophysique." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30318/document.
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Ces travaux de thèse concernent le développement de nouvelles méthodes de séparation aveugle de mélanges linéaires instantanés pour des applications à des données hyperspectrales en astrophysique. Nous avons proposé trois approches pour effectuer la séparation des données. Une première contribution est fondée sur l'hybridation de deux méthodes existantes de séparation aveugle de source (SAS) : la méthode SpaceCORR nécessitant une hypothèse de parcimonie et une méthode de factorisation en matrices non négatives (NMF). Nous montrons que l'utilisation des résultats de SpaceCORR pour initialiser la NMF permet d'améliorer les performances des méthodes utilisées seules. Nous avons ensuite proposé une première méthode originale permettant de relâcher la contrainte de parcimonie de SpaceCORR. La méthode MASS (pour \textit{Maximum Angle Source Separation}) est une méthode géométrique basée sur l'extraction de pixels mono-sources pour réaliser la séparation des données. Nous avons également étudié l'hybridation de MASS avec la NMF. Enfin, nous avons proposé une seconde approche permettant de relâcher la contrainte de parcimonie de SpaceCORR. La méthode originale SIBIS (pour \textit{Subspace-Intersection Blind Identification and Separation}) est une méthode géométrique basée sur l'identification de l'intersection de sous-espaces engendrés par des régions de l'image hyperspectrale. Ces intersections permettent, sous une hypothèse faible de parcimonie, de réaliser la séparation des données. L'ensemble des approches proposées dans ces travaux ont été validées par des tests sur données simulées puis appliquées sur données réelles. Les résultats obtenus sur ces données sont très encourageants et sont comparés à ceux obtenus par des méthodes de la littératureThis thesis deals with the development of new blind separation methods for linear instantaneous mixtures applicable to astrophysical hyperspectral data sets. We propose three approaches to perform data separation. A first contribution is based on hybridization of two existing blind source separation (BSS) methods: the SpaceCORR method, requiring a sparsity assumption, and a non-negative matrix factorization (NMF) method. We show that using SpaceCORR results to initialize the NMF improves the performance of the methods used alone. We then proposed a first original method to relax the sparsity constraint of SpaceCORR. The method called MASS (Maximum Angle Source Separation) is a geometric method based on the extraction of single-source pixels to achieve the separation of data. We also studied the hybridization of MASS with the NMF. Finally, we proposed an approach to relax the sparsity constraint of SpaceCORR. The original method called SIBIS (Subspace-Intersection Blind Identification and Separation) is a geometric method based on the identification of intersections of subspaces generated by regions of the hyperspectral image. Under a sparsity assumption, these intersections allow one to achieve the separation of the data. The approaches proposed in this manuscript have been validated by experimentations on simulated data and then applied to real data. The results obtained on our data are very encouraging and are compared with those obtained by methods from the literature
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Congedo, Marco. "EEG Source Analysis." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00880483.
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Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.
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Toumi, Ichrak. "Decomposition methods of NMR signal of complex mixtures : models ans applications." Electronic Thesis or Diss., Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4351.
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L'objectif de ce travail était de tester des méthodes de SAS pour la séparation des spectres complexes RMN de mélanges dans les plus simples des composés purs. Dans une première partie, les méthodes à savoir JADE et NNSC ont été appliqué es dans le cadre de la DOSY , une application aux données CPMG était démontrée. Dans une deuxième partie, on s'est concentré sur le développement d'un algorithme efficace "beta-SNMF" . Ceci s'est montré plus performant que NNSC pour beta inférieure ou égale à 2. Etant donné que dans la littérature, le choix de beta a été adapté aux hypothèses statistiques sur le bruit additif, une étude statistique du bruit RMN de la DOSY a été faite pour obtenir une image plus complète de nos données RMN étudiéesThe objective of the work was to test BSS methods for the separation of the complex NMR spectra of mixtures into the simpler ones of the pure compounds. In a first part, known methods namely JADE and NNSC were applied in conjunction for DOSY , performing applications for CPMG were demonstrated. In a second part, we focused on developing an effective algorithm "beta- SNMF ". This was demonstrated to outperform NNSC for beta less or equal to 2. Since in the literature, the choice of beta has been adapted to the statistical assumptions on the additive noise, a statistical study of NMR DOSY noise was done to get a more complete picture about our studied NMR data
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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.
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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal.In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.
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Gao, Bin. "Single channel blind source separation." Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1300.
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Single channel blind source separation (SCBSS) is an intensively researched field with numerous important applications. This research sets out to investigate the separation of monaural mixed audio recordings without relying on training knowledge. This research proposes a novel method based on variable regularised sparse nonnegative matrix factorization which decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral basis and temporal code of the sources. In this work, a variational Bayesian approach has been developed for computing the sparsity parameters of the matrix factorization. To further improve the previous work, this research proposes a new method based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMF). It is shown that IMFs have several desirable properties unique to SCBSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, this research develops a novel method for feature extraction using psycho-acoustic model. The monaural mixed signal is transformed to a cochleagram using the gammatone filterbank, whose bandwidths increase incrementally as the center frequency increases; thus resulting to non-uniform time-frequency (TF) resolution in the analysis of audio signal. Within this domain, a family of Itakura-Saito (IS) divergence based novel two-dimensional matrix factorization has been developed. The proposed matrix factorizations have the property of scale invariant which enables lower energy components in the cochleagram to be treated with equal importance as the high energy ones. Results show that all the developed algorithms presented in this thesis have outperformed conventional methods.
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Abrar, Shafayat. "Blind channel equalization and instantaneous blind source separation." Thesis, University of Liverpool, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540044.
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Khor, Li Chin. "Blind source separation under model misfits." Thesis, University of Newcastle upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490154.
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Blind Signal Separation (BSS) is a statistical signal processing-based technique and has recently been developed for many potential applications. This thesis aims to investigate model misfits in BSS problems as well as identify and develop efficient solutions for enhancing the performance of signal separation. This research sets out to investigate model misfits associated with finite signal sample size, mixing model, source signal and noise models. The effects of finite signal sample size on several well-known cost functions have been studied and this thesis has identified the most optimal cost function in separating signals with and without the presence of noise. A set of statistical tests is further developed to measure the performance in terms of speed, accuracy and convergence of the tested BSS algorithms. This work further explores the limitations of conventional assumptions of the noiseless and square mixing model which are often violated in practice and result in poor performance in signal separation. The separation of underdetermined mixing models as well as the assumptions of the source signals and noise are also addressed. This thesis presents the development of a Bayesian framework for underdetermined mixtures that produce accurate results in the estimation of mixing matrix and signals corrupted by noise. The proposed algorithm for underdetermined mixtures is capable of modelling a wide variety of signals ranging from unimodal to multimodal and symmetric to nonsymmetric signals. An integrated noise reduction procedure provides robustness against Gaussian noise and the commonly neglected non-Gaussian noise. Results justify the customisation of an algorithm for underdetermined mixtures and demonstrate the efficacy of the proposed algorithm which is three to five times better than existing algorithms. Finally, the work investigates another model misfit in the form of nonlinearly mixed signals and the difficulty of the problem. An algorithm that accurately separates nonlinear mixtures in the presence of noise is proposed. This algorithm features a system that maintains efficient convergence rate while minimising the risk of divergence regardless of the initialised parameters. There is also a mechanism that ameliorates global convergence. Results show that the proposed algorithm outperforms existing algorithms by at least three times with its features that simultaneously address the two crucial issues in the blind separation of nonlinear mixtures.
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Klajman, Maurice. "Mixed statistics in blind source separation." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406683.
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Zhou, Lihong. "Blind source separation systems for hearing aids." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28395.
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For many real-life situations, there is more than one speaker at a given time and people need to concentrate on a target sound signal to extract it. This process happens naturally for people with a normal hearing ability, but it is very difficult for hearing impaired persons. In this thesis, we present a system for enhancing the quality of the signal produced by a hearing aid. The proposed system combines spatial information with blind source separation (BSS) to extract the target signal. Results show that the proposed system can locate a target signal in different environments, with a good learning ability. The problem of locating and extracting a target source signal is first investigated. By applying a time-frequency masking method, it is then shown that the performance can be improved. Finally, the problem of underdetermined BSS is investigated and solved by combining a MVDR beamformer with a determined BSS system.
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Smith, Paul Carson. "Broadband analog opto-electronic blind source separation." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3178354.
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Badran, Salah Al-Din Ibrahim. "Efficient multiband algorithms for blind source separation." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/16089.
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The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme. This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme.
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Anemüller, Jörn. "Across-frequency processing in convolutive blind source separation." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962819247.
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Zhang, Jingyi. "Statistical blind source separation of post-nonlinear mixture." Thesis, University of Newcastle upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485858.
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Blind Source Separation (BSS) is a statistical signal processing technique and has recently been developed for many applications. The aim of this thesis is to investigate the blind signal separation problem under the environment where noise, reverberation and nonlinear distortion exist in the mixture and to develop novel solutions to solve the problem. The success and efficacy of the proposed algorithms is analysed in terms of robustness to noise, accuracy of recovered signal and speed of convergence. Linear BSS algorithms for instantaneous and convolutive mixtures are investigated and tested by a set of specially designed simulated experiments under various conditions. In addition, the post-nonlinear instantaneous mixture model has been critically researched and the theory of signal separability has been established. To overcome the limitation and drawbacks of the existing works on post-nonlinear mixture, a novel solution has been developed to separate noisy post-nonlinear instantaneous mixtures of non-stationary and temporally correlated sources and this work further extends to the case of noisy convolutive mixture. The proposed models allow source non-stationarity and temporal correlation to be incorporated into the new solutions. The Maximum Likelihood (ML) approach has been developed for both of the proposed algorithms to estimate the model parameters by the Expectation Maximisation (EM) algorithm and the post-nonlinearity is estimated by a set of self-updating polynomials whose coefficients are updated as part of the model parameters. The theoretical foundation of the proposed solutions has been rigorously developed and discussed in detail. The new algorithms have been tested by simulations using both synthetically generated and recorded speech signals to verify the accuracy and efficacy. The results show that the proposed algorithms outperform existing algorithms in the separation performance where significant improvement has been obtained.
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Liu, Xianhua Mechanical & Manufacturing Engineering Faculty of Engineering UNSW. "Blind source separation methods and their mechanical applications." Awarded by:University of New South Wales. School of Mechanical and Manufacturing Engineering, 2006. http://handle.unsw.edu.au/1959.4/24961.
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Blind Source Separation is a modern signal processing technique which recovers both the unknown sources and unknown mixing systems from only measured mixtures of signals. It has application in diverse fields such as communication, image processing, geological exploration and biomedical signal processing etc. This project studies the BSS problem, develop separation methods and reveal the potential for mechanical engineering applications. There are two models for blind source separation corresponding to the two ways that the sources are mixed, the instantaneous mixing model and the convolved mixing model. The author carried out a theoretical study of the first model by proposing an idea called Redundant Data Elimination which leads to geometric interpretation of the model, explains that circular distribution property is the reason why Gaussian signal mixtures can not be separated, and showed that this idea can improve separation accuracy for unsymmetrically distributed sources. This new idea enabled evaluation and comparison of two well-known algorithms and proposal of a simplified algorithm based on Joint Approximate Diagonalization of fourth order cumulant matrices, which is further developed by determining an optimized parameter value for separation convergence. Also based on the understanding from the RDE, an outlier spherical projection method is proposed to improve separation accuracy against outlier errors. Mechanical vibration or acoustic problems belong to the second model. After some theoretical study of the problem and the model, a novel application of the Blind Least Mean Square algorithm using Gray's variable norm as cost function is applied to engine vibration data to separate piston slap, fuel injection noise and cylinder pressure effects. Further, the algorithm is combined with a deflation algorithm for successive subtraction of recovered source responses from the measured mixture to enable the recovery of more sources. The algorithms are verified to be successful by simulation, and the separated engine sources are proved reasonable by analysing the engine operation and physical properties of the sources. The author also studied the relationship between these two models, the problems of different approaches for solving the model such as the frequency domain approach and the Bussgang approach, and sets out future research interests.
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Sansrimahachai, Puttachad. "Blind source separation algorithms for MIMO communication systems." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419916.
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Addison, W. D. "Blind source separation using spatial and temporal priors." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525254.
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Parathai, Phetcharat. "Blind source separation using statistical nonnegative matrix factorization." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/2830.
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Blind Source Separation (BSS) attempts to automatically extract and track a signal of interest in real world scenarios with other signals present. BSS addresses the problem of recovering the original signals from an observed mixture without relying on training knowledge. This research studied three novel approaches for solving the BSS problem based on the extensions of non-negative matrix factorization model and the sparsity regularization methods. 1) A framework of amalgamating pruning and Bayesian regularized cluster nonnegative tensor factorization with Itakura-Saito divergence for separating sources mixed in a stereo channel format: The sparse regularization term was adaptively tuned using a hierarchical Bayesian approach to yield the desired sparse decomposition. The modified Gaussian prior was formulated to express the correlation between different basis vectors. This algorithm automatically detected the optimal number of latent components of the individual source. 2) Factorization for single-channel BSS which decomposes an information-bearing matrix into complex of factor matrices that represent the spectral dictionary and temporal codes: A variational Bayesian approach was developed for computing the sparsity parameters for optimizing the matrix factorization. This approach combined the advantages of both complex matrix factorization (CMF) and variational-sparse analysis. An imitated-stereo mixture model developed by weighting and time-shifting the original single-channel mixture where source signals can be modelled by the AR processes. The proposed mixing mixture is analogous to a stereo signal created by two microphones with one being real and another virtual. The imitated-stereo mixture employed the nonnegative tensor factorization for separating the observed mixture. The separability analysis of the imitated-stereo mixture was derived using Wiener masking. All algorithms were tested with real audio signals. Performance of source separation was assessed by measuring the distortion between original source and the estimated one according to the signal-to-distortion (SDR) ratio. The experimental results demonstrate that the proposed uninformed audio separation algorithms have surpassed among the conventional BSS methods; i.e. IS-cNTF, SNMF and CMF methods, with average SDR improvement in the ranges from 2.6dB to 6.4dB per source.
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Leong, Wai Yie. "Implementing blind source separation in signal processing and telecommunications /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19158.pdf.
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Lösch, Benedikt [Verfasser]. "Complex Blind Source Separation with Audio Applications / Benedikt Lösch." München : Verlag Dr. Hut, 2013. http://d-nb.info/1042307806/34.
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Abadi, Bahador Makki. "New tensor factorization based approaches for blind source separation." Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543925.
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E, Okwelume Gozie, and Ezeude Anayo Kingsley. "BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS." Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.
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Our thesis work focuses on Frequency-domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied to instantaneous mixtures at each frequency bin. Computational complexity is also reduced by using this method. We also investigate the famous problem associated with Frequency-Domain Blind Source Separation using ICA referred to as the Permutation and Scaling ambiguities, using methods proposed by some researchers. This is our main target in this project; to solve the permutation and scaling ambiguities in real time applicationsGozie: modebelu2001@yahoo.com Anayo: ezeudea@yahoo.com
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Herrmann, Frank. "Independent component analysis with applications to blind source separation." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399147.
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Alphey, Marcus J. T. "Blind source separation : the effects of signal non-stationarity." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/11220.
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This thesis investigates the effect of non-stationarity reduction, in the form of silence removal, on the performance of blind separation and deconvolution techniques for speech signals. An information-maximisation-based system is used for the separation of instantaneously mixed signals, and a decorrelating system for convolutively mixed signals. An introduction to the concepts of adaptive signal processing, blind signal processing and artificial neural networks is presented. A review of approaches to solving the blind signal separation and deconvolution problems is provided. The susceptibility of the information-maximisation approach to signal non-stationarity is discussed and two methods of silence identification and removal are compared and used to pre-process data before blind separation. The "infomax" approach is used to separate instantaneous mixtures, and is also modified to incorporate silence assessment and removal techniques to form an on-line system. Further modifications are made to the algorithm to investigate the effect of alternative update strategies, and these are compared with experimental results from identical modifications to diverse separating algorithms. A performance metric is used to assess the quality of separation achieved. The application of these techniques to convolutively mixed speech signals is also investigated, using the CoB1iSS algorithm. The effectiveness of the application of the silence removal techniques to both the time domain and frequency domain representations of the outputs is tested. While this form of non-stationarity reduction improves the rate of convergence for instantaneous mixtures, it does not cause any significant improvement in separation performance under most of the experimental conditions tested. No significant difference in performance was noted for the separation of convolutive mixtures in either the time or frequency domain.
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Latif, Mohamed Amin. "Localization of brain signal sources using blind source separation." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/54567/.
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Reliable localization of brain signal sources by using convenient, easy, and hazardless data acquisition techniques can potentially play a key role in the understanding, analysis, and tracking of brain activities for determination of physiological, pathological, and functional abnormalities. The sources can be due to normal brain activities, mental disorders, stimulation of the brain, or movement related tasks. The focus of this thesis is therefore the development of novel source localization techniques based upon EEG measurements. Independent component analysis is used in blind separation (BSS) of the EEG sources to yield three different approaches for source localization. In the first method the sources are localized over the scalp pattern using BSS in various subbands, and by investigating the number of components which are likely to be the true sources. In the second method, the sources are separated and their corresponding topographical information is used within a least-squares algorithm to localize the sources within the brain region. The locations of the known sources, such as some normal brain rhythms, are also utilized to help in determining the unknown sources. The final approach is an effective BSS algorithm partially constrained by information related to the known sources. In addition, some investigation have been undertaken to incorporate non-homogeneity of the head layers in terms of the changes in electrical and magnetic characteristics and also with respect to the noise level within the processing methods. Experimental studies with real and synthetic data sets are undertaken using MATLAB and the efficacy of each method discussed.
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Guddeti, Ram Mohana Reddy. "Perceptually motivated blind source separation of convolutive audio mixtures." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/12073.
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The first objective of this thesis is to apply psycho-acoustic principles to the spatial processing of speech signals in noisy and reverberant environment. The key assumption that will be adopted is that modern signal processing has failed to mimic the cock-tail party effect because there has been no attempt to adequately incorporate the psycho acoustical phenomenon of audio masking to aid source separation. A quasi linear mechanism for mimicking simultaneous frequency masking and temporal masking (post masking) techniques are developed. This frame work is used to construct blind source separation algorithms that exploit audio masking prior to source separation (preprocessor) and after source separation (postprocessor). The final objective of this thesis is to exploit the perceptual irrelevancy of some of the input speech spectrum using the perceptual masking techniques before utilizing the subspace method as a preprocessor of the frequency-domain ICA (FDICA) which reduces the effect of room reflections in advance and the remaining direct sounds then being separated by ICA. Incorporating the perceptual masking techniques prior to the application of FDICA with the subspace method as preprocessor not only reduces the computational complexity of similarity measure for solving the permutations but also avoids the so-called permutation problem by targeting a specific speech signal more intelligible than the available microphone signals.
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Naqvi, Syed Mohsen Raza. "Multimodal methods for blind source separation of audio sources." Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/36117.
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The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance.
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Babaiezadeh, Malmiri Massoud. "On blind source separation in convolutive and nonlinear mixtures." Grenoble INPG, 2002. http://www.theses.fr/2002INPG0065.
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Dans cette thèse, la séparation aveugle de sources dans des mélanges convolutif Post Non-linéaire (CPNL) est étudiée. Pour séparer ce type de mélanges, nous avons d'abord développé des nouvelles méthodes pour séparer les mélanges convultifs et les mélanges Post Non-Linéaires (PNL). Ces méthodes sont toutes basées sur la minimisation de l'information mutuelle des sorties. Pour minimiser l'information mutuelle, nous calculons d'abord sa "différentielle", c'est-à-dire, sa variation en fonction d'une petite variation de son argument. Cette différentielle est alors utilisée pour concevoir des approches de type gradient pour minimiser l'information mutuelle des sorties. Ces approches peuvent être appliquées pour séparation aveugle des mélanges linéaires instantanés, convolutifs, PNL et CPNL.
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Riaz, Areeb. "Adaptive blind source separation based on intensity vector statistics." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/810208/.
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Human brain has the ability to focus on desired acoustic source when several sources are active. In the domain of digital electronics this problem is termed as the cock- tail party problem. Over the past few decades many algorithms have been proposed which attempt to solve this problem; they are generally termed as acoustic source separation algorithms. The proposed algorithms achieve separation of individual source components from observed acoustic mixtures. The source separation system may be capable of estimating the number of sources, their physical locations, the room impulse response and/or any target source signal information. A system that approximates this information is termed as blind. Source separation systems which require any such information beforehand are termed as semi-blind. Most of the proposed source separation algorithms deal with acoustic sources that are stationary in space. A more challenging task is to approximate unmixing filters while the sources are constantly moving. To maintain output performance in such a scenario, the source separation system has to swiftly and accurately detect the time variant mix- ing parameters, and update unmixing filters accordingly. The area of moving sources has still not been heavily investigated by researchers. The aim of this thesis is to further the field of acoustic source separation. Investigation of intensity vector direction (IVD) based source separation algorithm was carried out to analyse and improve the system, both in terms of applicability and output sound quality. The algorithm under investigation provides a robust and nearly closed-form solution to the source separation problem with a low processing time. However, the algorithm initially required unmixing filter coefficients as input for dealing with practical acoustic scenarios. Analysis performed with microphone array response, microphone array geometry and the room response yielded three different modifications to the baseline system, improving system applicability and output sound quality. The IVD based system was investigated to deal with more challenging acoustic scenarios, such as time variant number of sources. Likewise, the IVD statistics were analysed to propose solutions for moving sources scenario. The system exhibited potential to swiftly, accurately and reliably detect changes in the time varying mixing parameters. As a result of these investigations, a novel system pipeline is proposed, capable of detecting, tracking and separating moving sources in a blind manner. The proposed algorithms were evaluated for processing time and separation performance. Optimisation of output sound quality was carried out through objective performance measures, while speaker tracking was evaluated subjectively. Finally, a demonstration was developed in Matlab based on the proposed algorithms to facilitate user interaction with the surrounding acoustic environment.
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Kervazo, Christophe. "Optimization framework for large-scale sparse blind source separation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS354/document.
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Lors des dernières décennies, la Séparation Aveugle de Sources (BSS) est devenue un outil de premier plan pour le traitement de données multi-valuées. L’objectif de ce doctorat est cependant d’étudier les cas grande échelle, pour lesquels la plupart des algorithmes classiques obtiennent des performances dégradées. Ce document s’articule en quatre parties, traitant chacune un aspect du problème: i) l’introduction d’algorithmes robustes de BSS parcimonieuse ne nécessitant qu’un seul lancement (malgré un choix d’hyper-paramètres délicat) et fortement étayés mathématiquement; ii) la proposition d’une méthode permettant de maintenir une haute qualité de séparation malgré un nombre de sources important: iii) la modification d’un algorithme classique de BSS parcimonieuse pour l’application sur des données de grandes tailles; et iv) une extension au problème de BSS parcimonieuse non-linéaire. Les méthodes proposées ont été amplement testées, tant sur données simulées que réalistes, pour démontrer leur qualité. Des interprétations détaillées des résultats sont proposéesDuring the last decades, Blind Source Separation (BSS) has become a key analysis tool to study multi-valued data. The objective of this thesis is however to focus on large-scale settings, for which most classical algorithms fail. More specifically, it is subdivided into four sub-problems taking their roots around the large-scale sparse BSS issue: i) introduce a mathematically sound robust sparse BSS algorithm which does not require any relaunch (despite a difficult hyper-parameter choice); ii) introduce a method being able to maintain high quality separations even when a large-number of sources needs to be estimated; iii) make a classical sparse BSS algorithm scalable to large-scale datasets; and iv) an extension to the non-linear sparse BSS problem. The methods we propose are extensively tested on both simulated and realistic experiments to demonstrate their quality. In-depth interpretations of the results are proposed
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Zou, Liang. "Underdetermined joint blind source separation with application to physiological data." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63013.
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Blind Source Separation (BSS) methods have been attracting increasing attention for their promising applications in signal processing. Despite recent progress on the research of BSS, there are still remaining challenges. Specifically, this dissertation focuses on developing novel Underdetermined Blind Source Separation (UBSS) methods that can deal with several specific challenges in real applications, including limited number of observations, self/cross dependence information and source inference in the underdetermined case. First, by taking advantage of the
Noise Assisted Multivariate Empirical Mode Decomposition (NAMEMD) and Multiset Canonical Correlation Analysis (MCCA), we propose a novel BSS framework and apply it to extract the heart beat signal form noisy nano-sensor signals. Furthermore, we generalize the idea of (over)determined joint BSS to that of the underdetermined case. We explore the dependence information between two datasets and propose an underdetermined joint BSS method for two datasets, termed as UJBSS-2. In addition, by exploiting the cross correlation between each pair of datasets, we develop a novel and effective method to jointly estimate the mixing matrices
from multiple datasets, referred to as Underdetermined Joint Blind Source Separation for Multiple Datasets (UJBSS-M). In order to improve the time efficiency and relax the sparsity constraint, we recover the latent sources based on subspace representation when the mixing matrices are estimated. As an example application for noise enhanced signal processing, the proposed UJBSS-M method also can be utilized to solve the single-set UBSS problem when suitable noise is added to the observations. Finally, considering the recent increasing need for biomedical signal processing in the ambulatory environment, we propose a novel UBSS method for removing electromyogram (EMG) from Electroencephalography (EEG) signals. The proposed method for recovering the underlying sources is also applicable to other artifact removal problems. Simulation results demonstrate that the proposed methods yield superior performances over conventional approaches. We also evaluate the proposed methods on real physiological data, and the proposed methods are shown to effectively and efficiently recover the underlying sources.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Kvernelv, Vegard Berg. "Optimization on Matrix Manifolds with Applications to Blind Source Separation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for fysikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22688.
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Studere hvordan konsepter fra optimeringsteori generaliseres til mangfoldigheter, mer spesifikt matrisemangfoldigheter, og vurdere hvordan dette kan anvendes på "blind source separation"-problemer
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Wehr, Stefan [Verfasser]. "Robust Binaural Blind Source Separation in Hearing Aids / Stefan Wehr." München : Verlag Dr. Hut, 2013. http://d-nb.info/1031844627/34.
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Jafari, Maria Grazia. "Novel sequential algorithms for blind source separation of instantaneous mixtures." Thesis, King's College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397682.
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Remaggi, Luca. "Acoustic reflector localisation for blind source separation and spatial audio." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/842217/.
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From a physical point of view, sound is classically defined by wave functions. Like every other physical model based on waves, during its propagation, it interacts with the obstacles it encounters. These interactions result in reflections of the main signal that can be defined as either being supportive or interfering. In the signal processing research field, it is, therefore, important to identify these reflections, in order to either exploit or avoid them, respectively. The main contribution of this thesis focuses on the acoustic reflector localisation. Four novel methods are proposed: a method localising the image source before finding the reflector position; two variants of this method, which utilise information from multiple loudspeakers; a method directly localising the reflector without any pre-processing. Finally, utilising both simulated and measured data, a comparative evaluation is conducted among different acoustic reflector localisation methods. The results show the last proposed method outperforming the state-of-the-art. The second contribution of this thesis is given by applying the acoustic reflector localisation solution into spatial audio, with the main objective of enabling the listeners with the sensation of being in the recorded environment. A novel way of encoding and decoding the room acoustic information is proposed, by parametrising sounds, and defining them as reverberant spatial audio objects (RSAOs). A set of subjective assessments are performed. The results prove both the high quality of the sound produced by the proposed parametrisation, and the reliability on manually modifying the acoustic of recorded environments. The third contribution is proposed in the field of speech source separation. A modified version of a state-of-the-art method is presented, where the direct sound and first reflection information is utilised to model binaural cues. Experiments were performed to separate speech sources in different environments. The results show the new method to outperform the state-of-the-art, where one interferer is present in the recordings. The simulation and experimental results presented in this thesis represent a significant addition to the literature and will influence the future choices of acoustic reflector localisation systems, 3D rendering, and source separation techniques. Future work may focus on the fusion of acoustic and visual cues to enhance the acoustic scene analysis.
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Sudhakara, Murthy Prasad. "Sparse models and convex optimisation for convolutive blind source separation." Rennes 1, 2011. https://tel.archives-ouvertes.fr/tel-00586610.
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Blind source separation from underdetermined mixtures is usually a two-step process: the estimation of the mixing filters, followed by that of the sources. An enabling assumption is that the sources are sparse and disjoint in the time-frequency domain. For convolutive mixtures, the solution is not straightforward due to the permutation and scaling ambiguities. The sparsity of the filters in the time-domain is also an enabling factor for blind filter estimation approaches that are based on cross-relation. However, such approaches are restricted to the single source setting. In this thesis, we jointly exploit the sparsity of the sources and mixing filters for blind estimation of sparse filters from stereo convolutive mixtures of several sources. First, we show why the sparsity of the filters can help solve the permutation problem in convolutive source separation, in the absence of scaling. Then, we propose a twostage estimation framework, which is primarily based on the time-frequency domain cross-relation and an ℓ1 minimisation formulation: a) a clustering step to group the time-frequency points where only one source is active, for each source; b) a convex optimisation step which estimates the filters. The resulting algorithms are assessed on audio source separation and filter estimation problemsLa séparation aveugle de sources à partir de mélanges sous-déterminés se fait traditionnellement en deux étapes: l’estimation des filtres de mélange, puis celle des sources. L’hypothèse de parcimonie temps-fréquence des sources facilite la séparation, qui reste cependant difficile dans le cas de mélanges convolutifs à cause des ambiguités de permutation et de mise à l’échelle. Par ailleurs, la parcimonie temporelle des filtres facilite les techniques d’estimation aveugle de filtres fondées sur des corrélations croisées, qui restent cependant limitées au cas où une seule source est active. Dans cette thèse, on exploite conjointement la parcimonie des sources et des filtres de mélange pour l’estimation aveugle de filtres parcimonieux à partir de mélanges convolutifs stéréophoniques de plusieurs sources. Dans un premier temps, on montre comment la parcimonie des filtres permet de résoudre le problème de permutation, en l’absence de problème de mise à l’échelle. Ensuite, on propose un cadre constitu é de deux étapes pour l’estimation, basé sur des versions temps-fréquence de la corrélation croisée et sur la minimisation de norme ℓ1 : a) un clustering qui regroupe les points temps-fréquence où une seule source est active; b) la résolution d’un problème d’optimisation convexe pour estimer les filtres. La performance des algorithmes qui en résultent est évalués numériquement sur des problèmes de filtre d’estimation de filtres et de séparation de sources audio
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Roussos, Evangelos. "Bayesian methods for sparse data decomposition and blind source separation." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589766.
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In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or 'sources' via a generally unknown mapping. Reconstructing sources from their mixtures is an extremely ill-posed problem in general. However, solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian method- ology, allowing us to incorporate "soft" constraints in a natural manner. This Thesis proposes the use of sparse statistical decomposition methods for ex- ploratory analysis of datasets. We make use of the fact that many natural signals have a sparse representation in appropriate signal dictionaries. The work described in this Thesis is mainly driven by problems in the analysis of large datasets, such as those from functional magnetic resonance imaging of the brain for the neuro-scientific goal of extracting relevant 'maps' from the data. We first propose Bayesian Iterative Thresholding, a general method for solv- ing blind linear inverse problems under sparsity constraints, and we apply it to the problem of blind source separation. The algorithm is derived by maximiz- ing a variational lower-bound on the likelihood. The algorithm generalizes the recently proposed method of Iterative Thresholding. The probabilistic view en- ables us to automatically estimate various hyperparameters, such as those that control the shape of the prior and the threshold, in a principled manner. We then derive an efficient fully Bayesian sparse matrix factorization model for exploratory analysis and modelling of spatio-temporal data such as fMRI. We view sparse representation as a problem in Bayesian inference, following a ma- chine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. The performance and utility of the proposed algorithms is demonstrated on a variety of experiments using both simulated and real datasets. Experimental results with benchmark datasets show that the proposed algorithms outper- form state-of-the-art tools for model-free decompositions such as independent component analysis.
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Qi, Huan. "Video-based cardiac physiological measurements using joint blind source separation approaches." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54005.
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Non-contact measurements of human cardiopulmonary physiological parameters based on photoplethysmography (PPG) can lead to efficient and comfortable medical assessment. It was shown that human facial blood volume variation during cardiac cycle can be indirectly captured by regular Red-Green-Blue (RGB) cameras. However, few attempts have been made to incorporate data from different facial sub-regions to improve remote measurement performance. In this thesis, we propose a novel framework for non-contact video-based human heart rate (HR) measurement by exploring correlations among facial sub-regions via joint blind source separation (J-BSS). In an experiment involving video data collected from 16 subjects, we compare the non-contact HR measurement results obtained from a commercial digital camera to results from a Health Canada and Food and Drug Administration (FDA) licensed contact blood volume pulse (BVP) sensor. We further test our framework on a large public database, which provides subjects' left-thumb plethysmograph signal as ground truth. Experimental results show that the proposed framework outperforms the state-of-the-art independent component analysis (ICA)-based methodologies. Driver physiological monitoring in vehicle is of great importance to provide a comfortable driving environment and prevent road accidents. Contact sensors can be placed on the driver's body to measure various physiological parameters. However such sensors may cause discomfort or distraction. The development of non-contact techniques can provide a promising solution. In this thesis, we employ our proposed non-contact video-based HR measurement framework to monitor the drivers heart rate and do heart rate variability analysis using a simple consumer-level webcam. Experiments of real-world road driving demonstrate that the proposed non-contact framework is promising even with the presence of unstable illumination variation and head movement.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Choi, Hyung Keun. "Blind source separation of the audio signals in a real world." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/14986.
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Abolghasemi, Vahid. "Advances in compressive sensing and its application in blind source separation." Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543283.
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Kiani, Saeed. "Blind source separation in dynamic contrast enhanced magnetic resonance imaging renography." Thesis, University of Surrey, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616917.
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Dynamic contrast~enhanced magnetic resonance imaging (DCE-MRI) renography is a desirable kidney assessment methodology owing to the lack of ionizing radiation in MRI and its capability of producing high-resolution anatomical image data as well as physiological data. DCE-MRI renography emerged with the view to provide a minimally invasive framework to quickly and accurately assess kidney function, for example, to measure glomerular filtration rate (GFR). However, despite considerable developments, it is not yet considered a robust technique of renal assessment. This is due to a number of confounding factors ranging from · optimization of data acquisition parameters to data post-processing challenges such as organ motion (mainly due to breathing), segmentation, partial volume (PV) effect (a signal mixing phenomenon) and tracer kinetic modelling. Prior works including registration-based motion correction techniques, semi-automatic segmentation based on similarity measures and a template-based PV correction method have not provided a complete and practical solution. In this work, a blind source separation (BSS) approach based on time-delayed decorrelation and temporal independent component analysis (ICA) was proposed to unmix physiological signals and remove the undesired motion artefacts. To evahtate the technique, test data were constructed using kidney, liver and non- . specific tissue dynamic MR signals. The source signals were correctly identified with small errors and coefficient of determination r2 values of 0.85 - 0.99 between the independent components (ICs) and source signals.
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Stokes, Tobias W. "Improving the perceptual quality of single-channel blind audio source separation." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/807786/.
Full textAbstract:
Given a mixture of audio sources, a blind audio source separation (BASS) tool is required to extract audio relating to one specific source whilst attenuating that related to all others. This thesis answers the question “How can the perceptual quality of BASS be improved for broadcasting applications?” The most common source separation scenario, particularly in the field of broadcasting, is single channel, and this is particularly challenging as a limited set of cues are available. Broadcasting also requires that a source separator is automated, capable of handling non-stationary, reverberant mixtures and able to separate an unknown number of sources. In the single-channel case, the time- frequency mask is common as a method of separation. However, this process produces artefacts in the separated audio. The perceptual evaluation for audio source separation (PEASS) toolkit represents an efficient way to generate a multi-dimensional measure of perceptual quality. Initial experimental work, using ideal target and interferer estimates, uses PEASS to test variations on the ideal binary mask and shows continuous masks are perceptually better than binary while identifying a trade-off between artefacts and interferer suppression. To explore the optimisation of this trade-off, a series of sigmoidal functions are used to map target-to-mixture ratios to mask coefficients. This leads to a mask, with less target-to-mixture based discrimination than those typically found in literature, being identified as the optimum. Further experiments applying offsets, hysteresis, smoothing and frequency-dependency to the mask do not show any benefit in audio quality. The optimal sigmoidal mask is demonstrated to also be superior under non-ideal conditions using a non-negative matrix factorisation algorithm to produce the estimates. A final listening test compares the outputs of binary, ratio and optimal sigmoidal masks concluding that listeners prefer the ratio mask to the sigmoidal mask and both continuous masks to the binary mask.
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Lee, In Tae. "Machine learning algorithms for independent vector analysis and blind source separation." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3373454.
Full textAbstract:
Thesis (Ph. D.)--University of California, San Diego, 2009.Title from first page of PDF file (viewed October 22, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 59-63) and index.
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Domingo, Almenara Xavier. "Automated mass spectrometry-based metabolomics data processing by blind source separation methods." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/397799.
Full textAbstract:
Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A
més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes
caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar
estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes
existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu
d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en
metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a
resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser
desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir
automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i
adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A
més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes
caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar
estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes
existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu
d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en
metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a
resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser
desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir
automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i
adquirir nous coneixements biològics.Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.
One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights.