Probabilistic generative modeling of speech

Speech processing refers to a set of tasks that involve speech analysis and synthesis. Most speech processing algorithms model a subset of speech parameters of interest and blur the rest using signal processing techniques and feature extraction. However, evidence shows that many speech parameters can be more accurately estimated if they are modeled jointly; speech synthesis also benefits from joint modeling. This thesis proposes a probabilistic generative model for speech called the Probabilistic Acoustic Tube (PAT). The highlights of the model are threefold. First, it is among the very first works to build a complete probabilistic model for speech. Second, it has a well-designed model for the phase spectrum of speech, which has been hard to model and often neglected. Third, it models the AM-FM effects in speech, which are perceptually significant but often ignored in frame-based speech processing algorithms. Experiment shows that the proposed model has good potential for a number of speech processing tasks

Data Fusion for MaaS: Opportunities and Challenges

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc

Learning spatial correlations for Bayesian fusion in pipe thickness mapping

© 2014 IEEE. Pipe thickness maps are used to assess the condition in pipelines. Thickness maps are a 2.5D representation similar to elevation maps in robotics. Probabilistic frameworks, however, have barely been used in this context. This paper presents a general approach for generating probabilistic maps from heterogeneous sensor data. The key idea is to learn the spatial correlation of a sensor through Gaussian Process models and use it as priors for Bayesian fusion. This approach is applied to the novel application of pipe thickness mapping. Data from a 3D laser scanner on the outer surface of the pipe and thickness measurements from a contact ultrasonic sensor are fused into a single thickness map with associated uncertainty. Moreover, a dedicated algorithm to model the ultrasonic sensor using kernel density estimation is also proposed. The overall approach is evaluated using the full 3D profile (outer and inner surfaces) of the pipe section as ground truth

Estimation and Control of Traffic Relying on Vehicular Connectivity

Vehicular traffic flow is essential, yet complicated to analyze. It describes the interplay among vehicles and with the infrastructure. A better understanding of traf-fic would benefit both individuals and the whole society in terms of improving safety, energy efficiency, and reducing environmental impacts. A large body of research ex-ists on estimation and control of vehicular traffic in which, however, vehicles were assumed not to be able to share information due to the limits of technology. With the development of wireless communication and various sensor devices, Connected Vehicles(CV) are emerging which are able to detect, access, and share information with each other and with the infrastructure in real time. Connected Vehicle Technology (CVT) has been attracting more and more attentions from different fields. The goal of this dissertation is to develop approaches to estimate and control vehicular traffic as well as individual vehicles relying on CVT. On one hand, CVT sig-nificantly enriches the data from individuals and the traffic, which contributes to the accuracy of traffic estimation algorithms. On the other hand, CVT enables commu-nication and information sharing between vehicles and infrastructure, and therefore allows vehicles to achieve better control and/or coordination among themselves and with smart infrastructure. The first part of this dissertation focused on estimation of traffic on freeways and city streets. We use data available from on road sensors and also from probe One of the most important traffic performance measures is travel time. How-ever it is affected by various factors, and freeways and arterials have different travel time characteristics. In this dissertation we first propose a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribu-tion for travel time. The probability distribution is generated using a Monte Carlo simulation and an Online Expectation Maximization clustering algorithm. Results show that the approach is able to generate a reasonable multimodal distribution for travel-time. For arterials, this dissertation presents methods for estimating statistics of travel time by utilizing sparse vehicular probe data. A public data feed from transit buses in the City of San Francisco is used. We divide each link into shorter segments, and propose iterative methods for allocating travel time statistics to each segment. Inspired by K-mean and Expectation Maximization (EM) algorithms, we iteratively update the mean and variance of travel time for each segment based on historical probe data until convergence. Based on segment travel time statistics, we then pro-pose a method to estimate the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. The results are compared to high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach. The second part of this dissertation emphasize on control approaches enabled by vehicular connectivity. Estimation and prediction of surrounding vehicle behaviors and upcoming traffic makes it possible to improve driving performance. We first propose a Speed Advisory System for arterial roads, which utilizes upcoming traffi

Clock Synchronization in Wireless Sensor Networks: An Overview

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs