Blind source separation using dictionary learning over time-varying channels

Distributed sensors observe radio frequency (RF) sources over flat-fading channels. The activity pattern is sparse and intermittent in the sense that while the number of latent sources may be larger than the number of sensors, only a few of them may be active at any particular time instant. It is further assumed that the source activity is modeled by a Hidden Markov Model. In previous work, the Blind Source Separation (BSS) problem solved for stationary channels using Dictionary Learning (DL). This thesis studies the effect of time-varying channels on the performance of DL algorithms. The performance metric is the probability of detection, where a correct detection is the event that the estimated value of a source exceeds a threshold at a time instant when the true source is active. Using the probability of detection when the channels are stationary as a baseline, it is shown that there is significant degradation for time-varying channels and observation intervals much longer than the time coherence. Detection performance improves when the observation time is approximately equal to the time coherence. Performance is again degraded when the observation is shorter and there is not sufficient information for the DL algorithms to learn from

Blind source separation using statistical nonnegative matrix factorization

PhD ThesisBlind 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. BLIND SOURCE SEPARATION USING STATISTICAL NONNEGATIVE MATRIX FACTORIZATION ii 3) 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.Payap Universit

Temperature-Driven Anomaly Detection Methods for Structural Health Monitoring

Reported in this thesis is a data-driven anomaly detection method for structural health monitoring which is based on the utilization of temperature-induced variations. Structural anomaly detection should be able to identify meaningful changes in measurements which are due to structural abnormal behaviour. Because, the temperature-induced variations and structural abnormalities may produce significant misinterpretations, the development of solutions to identify a structural anomaly, accounting for temperature influence, from measurements, is a critical procedure to support structural maintenance. A temperature-driven anomaly detection method is proposed, that introduces the idea of blind source separation for extracting thermal response and for further anomaly detection. Two thermal feature extraction methods are employed corresponding to the classification of underdetermined and overdetermined methods. The underdetermined method has the three phases of: (a) mode decomposition by utilising Empirical Mode Decomposition or Ensemble Empirical Mode Decomposition; (b) data reduction by performing Principal Component Analysis (PCA); (c) blind separation by applying Independent Component Analysis (ICA). The overdetermined method has the two stages of the pre-indication according to PCA and the blind separation by the devotion of ICA. Based on the extracted thermal response, the temperature-driven anomaly detection method is later developed in combination with the four methodologies of: Moving Principal Component Analysis (MPCA); Robust Regression Analysis (RRA); One-Class Support Vector Machine (OCSVM); Artificial Neural Network (ANN). Therefore, the proposed temperature-driven anomaly detection methods are designed as Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN. The proposed thermal feature extraction methods and temperature-driven anomaly detection methods have been investigated in the context of three case studies. The first case is a numerical truss bridge with simulated material stiffness reduction to create levels of damage. The second case is a purpose constructed truss bridge in the Structures Lab at the University of Warwick. The third case study is Ricciolo curved viaduct in Switzerland. Two primary findings can be confirmed from the evaluation results of these three case studies. Firstly, temperature-induced variations can conceal damage information in measurements. Secondly, the detection abilities of temperature-driven methods, which are Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN, for disclosing slight anomalies in time are more efficient when compared with the current anomaly detection method, which are MPCA, RRA, OCSVM, and ANN. The unique features of the author’s proposed temperature-driven anomaly detection method can be highlighted as follows: (a) it is a data-driven method for extracting features from an unknown structural system. In another word, the prior knowledge of the structural in-service conditions and physical models are not necessary; (b) it is the first time that blind source separation approaches and relative algorithms have been successfully employed for extracting temperature-induced responses; (c) it is a new approach to reliably assess the capability of using temperature-induced responses for anomaly detection

Statistical single channel source separation

PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields in signal processing and has various significant applications. Unlike conventional SCSS methods which were based on linear instantaneous model, this research sets out to investigate the separation of single channel in two types of mixture which is nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear SCSS in instantaneous mixture, this research proposes a novel solution based on a two-stage process that consists of a Gaussianization transform which efficiently compensates for the nonlinear distortion follow by a maximum likelihood estimator to perform source separation. For linear SCSS in convolutive mixture, this research proposes new methods based on nonnegative matrix factorization which decomposes a mixture into two-dimensional convolution factor matrices that represent the spectral basis and temporal code. The proposed factorization considers the convolutive mixing in the decomposition by introducing frequency constrained parameters in the model. The method aims to separate the mixture into its constituent spectral-temporal source components while alleviating the effect of convolutive mixing. In addition, family of Itakura-Saito divergence has been developed as a cost function which brings the beneficial property of scale-invariant. Two new statistical techniques are proposed, namely, Expectation-Maximisation (EM) based algorithm framework which maximizes the log-likelihood of a mixed signals, and the maximum a posteriori approach which maximises the joint probability of a mixed signal using multiplicative update rules. To further improve this research work, a novel method that incorporates adaptive sparseness into the solution has been proposed to resolve the ambiguity and hence, improve the algorithm performance. The theoretical foundation of the proposed solutions has been rigorously developed and discussed in details. Results have concretely shown the effectiveness of all the proposed algorithms presented in this thesis in separating the mixed signals in single channel and have outperformed others available methods.Universiti Teknikal Malaysia Melaka(UTeM), Ministry of Higher Education of Malaysi

Underdetermined convolutive source separation using two dimensional non-negative factorization techniques

PhD ThesisIn this thesis the underdetermined audio source separation has been considered, that is, estimating the original audio sources from the observed mixture when the number of audio sources is greater than the number of channels. The separation has been carried out using two approaches; the blind audio source separation and the informed audio source separation. The blind audio source separation approach depends on the mixture signal only and it assumes that the separation has been accomplished without any prior information (or as little as possible) about the sources. The informed audio source separation uses the exemplar in addition to the mixture signal to emulate the targeted speech signal to be separated. Both approaches are based on the two dimensional factorization techniques that decompose the signal into two tensors that are convolved in both the temporal and spectral directions. Both approaches are applied on the convolutive mixture and the high-reverberant convolutive mixture which are more realistic than the instantaneous mixture. In this work a novel algorithm based on the nonnegative matrix factor two dimensional deconvolution (NMF2D) with adaptive sparsity has been proposed to separate the audio sources that have been mixed in an underdetermined convolutive mixture. Additionally, a novel Gamma Exponential Process has been proposed for estimating the convolutive parameters and number of components of the NMF2D/ NTF2D, and to initialize the NMF2D parameters. In addition, the effects of different window length have been investigated to determine the best fit model that suit the characteristics of the audio signal. Furthermore, a novel algorithm, namely the fusion K models of full-rank weighted nonnegative tensor factor two dimensional deconvolution (K-wNTF2D) has been proposed. The K-wNTF2D is developed for its ability in modelling both the spectral and temporal changes, and the spatial covariance matrix that addresses the high reverberation problem. Variable sparsity that derived from the Gibbs distribution is optimized under the Itakura-Saito divergence and adapted into the K-wNTF2D model. The tensors of this algorithm have been initialized by a novel initialization method, namely the SVD two-dimensional deconvolution (SVD2D). Finally, two novel informed source separation algorithms, namely, the semi-exemplar based algorithm and the exemplar-based algorithm, have been proposed. These algorithms based on the NMF2D model and the proposed two dimensional nonnegative matrix partial co-factorization (2DNMPCF) model. The idea of incorporating the exemplar is to inform the proposed separation algorithms about the targeted signal to be separated by initializing its parameters and guide the proposed separation algorithms. The adaptive sparsity is derived for both ii of the proposed algorithms. Also, a multistage of the proposed exemplar based algorithm has been proposed in order to further enhance the separation performance. Results have shown that the proposed separation algorithms are very promising, more flexible, and offer an alternative model to the conventional methods

Single channel blind source separation

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Gaussian Framework for Interference Reduction in Live Recordings

Here typical live full-length music recordings are considered. In this scenarios, some instrumental voices are captured by microphones intended to other voices, leading to so-called “interferences”. Reducing this phenomenon is desirable because it opens new possibilities for sound engineers and also it has been proven that it increase performances of music analysis and processing tools (e.g. pitch tracking). In this work we propose an fast NMF-based algorithm to solve this problem.ope

Advances in independent component analysis with applications to data mining

This thesis considers the problem of finding latent structure in high dimensional data. It is assumed that the observed data are generated by unknown latent variables and their interactions. The task is to find these latent variables and the way they interact, given the observed data only. It is assumed that the latent variables do not depend on each other but act independently. A popular method for solving the above problem is independent component analysis (ICA). It is a statistical method for expressing a set of multidimensional observations as a combination of unknown latent variables that are statistically independent of each other. Starting from ICA, several methods of estimating the latent structure in different problem settings are derived and presented in this thesis. An ICA algorithm for analyzing complex valued signals is given; a way of using ICA in the context of regression is discussed; and an ICA-type algorithm is used for analyzing the topics in dynamically changing text data. In addition to ICA-type methods, two algorithms are given for estimating the latent structure in binary valued data. Experimental results are given on all of the presented methods. Another, partially overlapping problem considered in this thesis is dimensionality reduction. Empirical validation is given on a computationally simple method called random projection: it does not introduce severe distortions in the data. It is also proposed that random projection could be used as a preprocessing method prior to ICA, and experimental results are shown to support this claim. This thesis also contains several literature surveys on various aspects of finding the latent structure in high dimensional data.reviewe