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

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal separation is presented. NMF2D is a popular approach for retrieving low-rank approximations of nonnegative data such as image pixel, audio signal, data mining, pattern recognition and so on. In this paper, we concentrate on biomedical signal separation by using NMF2D with alpha-divergence family which decomposes a mixture into two-dimensional convolution factor matrices that represent temporal code and the spectral basis. The proposed iterative estimation algorithm (alpha-divergence algorithm) is initialized with random values, and it updated using multiplicative update rules until the values converge. Simulation experiments were carried out by comparing the original and estimated signal in term of signal-to-distortion ratio (SDR). The performances have been evaluated by including and excluding the sparseness constraint which sparseness is favored by penalizing nonzero gains. As a result, the proposed algorithm improved the iteration speed and sparseness constraints produce slight improvement of SDR

Performance Analysis between Basic Block Matching and Dynamic Programming of Stereo Matching Algorithm

One of the most important key steps of stereo vision algorithms is the disparity map implementation, where it generally utilized to decorrelate data and recover 3D scene framework of stereo image pairs. However, less accuracy of attaining the disparity map is one of the challenging problems on stereo vision approach. Thus, various methods of stereo matching algorithms have been developed and widely investigated for implementing the disparity map of stereo image pairs including the Dynamic Programming (DP) and the Basic Block Matching (BBM) methods. This paper mainly presents an evaluation between the Dynamic Programming (DP) and the Basic Block Matching (BBM) methods of stereo matching algorithms in term of disparity map accuracy, noise enhancement, and smoothness. Where the Basic Block Matching (BBM) is using the Sum of Absolute Difference (SAD) method in this research as a basic algorithm to determine the correspondence points between the target and reference images. In contrast, Dynamic Programming (DP) has been used as a global optimization approach. Besides, there will be a performance analysis including graphs results from both methods presented in this paper, which can show that both methods can be used on many stereo vision applications

Performance Analysis on Stereo Matching Algorithms Based on Local and Global Methods for 3D Images Application

Stereo matching is one of the methods in computer vision and image processing. There have numerous algorithms that have been found associated between disparity maps and ground truth data. Stereo Matching Algorithms were applied to obtain high accuracy of the depth as well as reducing the computational cost of the stereo image or video. The smoother the disparity depth map, the better results of triangulation can be achieved. The selection of an appropriate set of stereo data is very important because these stereo pairs have different characteristics. This paper discussed the performance analysis on stereo matching algorithm through Peak Signal to Noise Ratio (PSNR in dB), Structural Similarity (SSIM), the effect of window size and execution time for different type of techniques such as Sum Absolute Differences (SAD), Sum Square Differences (SSD), Normalized Cross Correlation (NCC), Block Matching (BM), Global Error Energy Minimization by Smoothing Functions, Adapting BP and Dynamic Programming (DP). The dataset of stereo images that used for the experimental purpose is obtained from Middlebury Stereo Datasets

Adaptive Diamond Search Algorithm for Motion Estimation

Implementation of the Block Matching Algorithm (BMA) in Motion Estimation (ME) has been widely used in video encoder due to its simplicity and high compression efficiency. Many fast search methods of BMAs are being introduced to increase the efficiency  of the ME  process. This paper proposed a new algorithm, namely Adaptive Diamond Search Algorithm (ADS) which employs three different search patterns for its two main stages. At the initial step, an additional step is added to a predetermined static block to further speed up the search process as it is beneficial to small motion video sequence contents. The performances of the ADS are then compared with three selected established algorithms, namely the Full Search (FS), Diamond Search (DS) and Hexagon-Diamond Search (HDS). Based on the simulation result, the proposed algorithm yields a very good video quality performance with fewer search points compared with other algorithms

Low Power Operational Amplifier In 0.13um Technology

Low power is one of the most indispensable criteria in several of application. In this paper a low power operational amplifier consists of two stages and operates at 1.8V power. It is designed to meet a set of provided specification such as high gain and low power consumption. Designers are able to work at low input bias current and also at low voltage due to the unique behavior of the MOS transistors in sub-threshold region. This two-stage op-amp is designed using the Silterra 130nm technology library. The layout has been draw and its area had been calculated. The proposed two stage op-amp consists of NMOS current mirror as bias circuit, differential amplifier as the first stage and common source amplifier as the second stage. The first stage of an op-amp contributed high gain while the second stage contributes a moderate gain. The results show that the circuit is able to work at 1.8V power supply voltage (VDD) and provides gain of 69.73dB and 28.406MHz of gain bandwidth product for a load of 2pF capacitor. Therefore, the power dissipation and the consistency of this operational amplifier are better than previously reported operational amplifier

Indoor Location Estimation Utilizing Wi-Fi Signals

Global Positioning System is commonly been used for locating a position of a specific structure in finding geographical coordinates of a target area. Though, this application is still having a restricted in term of the signals, might not well operated and ineffective for indoor usage. The study aim is to develop positioning and localization systems by using Wi-Fi signal. Estimation was made based on the measurement of wireless distance for estimation the user’s coordinates. Analysis of views called the fingerprint algorithm is used in this study. The algorithm involved two phases over an offline and the online phases of the survey. Unidentified user’s coordinates will be in the online phase by comparative databases collected in the survey phase. MATLAB Graphical User Interface and Android has been used to develop a user interface for simulation purposes. Several analyses were performed to define the precision and efficiency of occurred error as the number of access points and the traffic environment. Finally, the user required to provide several inputs e.g. the exact location and the RSS from AP’s number at the present location. The simulation-based software will evaluate the estimation location and positioning of the user and will match to user’s precise locatio

Nonlinear single channel source separation

A new model of nonlinear single channel source separation is proposed in this paper. The proposed model is a linear mixture of the independent sources followed by an element-wise post-nonlinear distortion function. In addition, the paper develops a novel solution that efficiently compensates for the nonlinear distortion and performs source separation. The proposed solution is a two-stage process that consists of a Gaussianization transform and a maximum likelihood estimator for the sources. The paper also discusses the theory behind the proposed solution. Simulations have been carried out to verify the theory and evaluate the performance of the proposed algorithm. Results obtained have shown the effectiveness of the algorithm even in presence of the strong nonlinearity

Blind Audio Source Separation with Sparse Nonnegative Matrix Factorization

In this study, a new technique in source separation using Two-Dimensional Nonnegative Matrix Factorization (NMF2D) with the Beta-divergence is proposed. The Time-Frequency (TF) profile of each source is modeled as two-dimensional convolution of the temporal code and the spectral basis. In addition, adaptive sparsity constraint was imposed to reduce the ambiguity and provide uniqueness to the solution. The proposed model used Beta-divergence as a cost function and updated by maximizing the joint probability of the mixing spectral basis and temporal codes using the multiplicative update rules. Experimental tests have been conducted in audio application to blindly separate the source in musical mixture. Results have shown the effectiveness of the algorithm in separating the audio sources from single channel mixture