Machine learning and inferencing for the decomposition of speech mixtures

In this dissertation, we present and evaluate a novel approach for incorporating machine learning and inferencing into the time-frequency decomposition of speech signals in the context of speaker-independent multi-speaker pitch tracking. The pitch tracking performance of the resulting algorithm is comparable to that of a state-of-the-art machine-learning algorithm for multi-pitch tracking while being significantly more computationally efficient and requiring much less training data. Multi-pitch tracking is a time-frequency signal processing problem in which mutual interferences of the harmonics from different speakers make it challenging to design an algorithm to reliably estimate the fundamental frequency trajectories of the individual speakers. The current state-of-the-art in speaker-independent multi-pitch tracking utilizes 1) a deep neural network for producing spectrograms of individual speakers and 2) another deep neural network that acts upon the individual spectrograms and the original audio’s spectrogram to produce estimates of the pitch tracks of the individual speakers. However, the implementation of this Multi-Spectrogram Machine- Learning (MS-ML) algorithm could be computationally intensive and make it impractical for hardware platforms such as embedded devices where the computational power is limited. Instead of utilizing deep neural networks to estimate the pitch values directly, we have derived and evaluated a fault recognition and diagnosis (FRD) framework that utilizes machine learning and inferencing techniques to recognize potential faults in the pitch tracks produced by a traditional multi-pitch tracking algorithm. The result of this fault-recognition phase is then used to trigger a fault-diagnosis phase aimed at resolving the recognized fault(s) through adaptive adjustment of the time-frequency analysis of the input signal. The pitch estimates produced by the resulting FRD-ML algorithm are found to be comparable in accuracy to those produced via the MS-ML algorithm. However, our evaluation of the FRD-ML algorithm shows it to have significant advantages over the MS-ML algorithm. Specifically, the number of multiplications per second in FRD-ML is found to be two orders of magnitude less while the number of additions per second is about the same as in the MS-ML algorithm. Furthermore, the required amount of training data to achieve optimal performance is found to be two orders of magnitude less for the FRD-ML algorithm in comparison to the MS-ML algorithm. The reduction in the number of multiplications per second means it is more feasible to implement the MPT solution on hardware platforms with limited computational power such as embedded devices rather than relying on Graphics Processing Units (GPUs) or cloud computing. The reduction in training data size makes the algorithm more flexible in terms of configuring for different application scenarios such as training for different languages where there may not be a large amount of training data

Semi-varying coefficient multinomial logistic regression for disease progression risk prediction

This paper proposes a risk prediction model using semi-varying coefficient multinomial logistic regression. We use a penalized local likelihood method to do the model selection and estimate both functional and constant coefficients in the selected model. The model can be used to improve predictive modelling when non-linear interactions between predictors are present. We conduct a simulation study to assess our method's performance, and the results show that the model selection procedure works well with small average numbers of wrong-selection or missing-selection. We illustrate the use of our method by applying it to classify the patients with early rheumatoid arthritis at baseline into different risk groups in future disease progression. We use a leave-one-out cross-validation method to assess its correct prediction rate and propose a recalibration framework to evaluate how reliable are the predicted risks

Structure Identification in Panel Data Analysis

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings

A Quantitative Seismic Behavior Assessment of Buried Structures

This dissertation is focused on quantitatively investigating the nonlinear seismic behavior assessment of underground structures, by performing high-fidelity SSI analyses. Specifically, several computer codes are developed for forward simulation of wave propagation in both two- (plane-strain) and three-dimensional semi-infinite heterogeneous solid media. (i) a multi-axial bounding surface plasticity model is implemented, calibrated and validated through centrifuge test data, to consider the soil nonlinearities (ii) the domain reduction method (DRM) is implemented for both 2D and 3D domains, homogeneous and heterogeneous media, vertical and inclined incident SV waves, to consistently prescribe the input motions in a truncated domain and (iii) perfectly matched layer (PML) is implemented for both 2D and 3D domains, to absorb the outgoing waves super efficiently.By using the aforementioned numerical tools, multiple studies on seismic behavior assessment of underground structures are performed.1. Development of validated methods for soil-structure analysis of buried structures. State-of-the-art versions of these simplified methods of seismic analysis for buried/embedded structures were most recently articulated in the “NCHRP 611” report, and comparisons of their predictions to experimental data are made in the present study in order to establish the validity (or lack thereof) of this method. Experiments comprises centrifuge tests on two specimens—one relatively- stiff rectangular and one relatively-flexible circular culvert—embedded in dense dry sand. Comparisons of experimental data are also made with predictions from a calibrated two-dimensional (plane-strain) finite element (FE) model. Predictions made using this FE model are superior and exhibits acceptable errors.2. Parametric studies of buried circular structures and a proposed improvement of the NCHRP 611 method. The NCHRP 611 method has been widely adopted as a guideline in the analysis design of buried/embedded structures due to its computational simplicity and broadly accepted accuracy for simple soil-structure configurations. However, the method is not without shortcomings. In particular, the NCHRP method is not sensitive to the inherently broadband frequency content of seismic input excitations, soil heterogeneities, and potential kinematic interaction effects. The present study seeks to quantitatively assess the brackets of the validity of the NCHRP 611 method—specifically, for soil-structure analyses of buried circular structures, and offers an improvement that is simple to implement. This is achieved through parametric studies using detailed nonlinear finite element simulations involving a broad range of ground motions, and soil and structural properties. The simulations are carried out with a model that has been validated in a prior centrifuge testing program on embedded structures. A refined version of the NCHRP 611 method, which uses maximum shear strains obtained through one-dimensional site response analyses, is shown to produce fairly accurate results for nearly all of the different cases considered in the parametric studies.3. Fragility-based seismic performance assessment of buried structures. Fragility-based seismic performance assessment and design procedures are being refined and adopted for many civil structures. With recent advances in computational capabilities as well as broad improvements in ground motion characterization and inelastic modeling of structural and geotechnical systems, large-scale direct models for underground structures—e.g., tunnels, water reservoirs, etc.—can now be devised with relative ease and deployed in engineering practice. In this study, a fragility-based seismic performance assessment of a large buried circular culvert is presented. Existing documents/codes are used to define the performance criteria and develop fragility functions through a Probabilistic Seismic Demand Analysis (PSDA) procedure. The analyses incorporate nonlinear behavior of soils and structural components, various soil layer profiles and account for uncertainties in the expected ground motions