6 research outputs found
Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline
The use of modern vocoders in an analysis/synthesis pipeline allows us to
investigate high-quality voice conversion that can be used for privacy
purposes. Here, we propose to transform the speaker embedding and the pitch in
order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation
fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis
approach, which is consistent with the zero-evidence concept of privacy. This
approach significantly reduces the information in speech related to the
speaker's sex while preserving speech content and some consistency in the
resulting protected voices.Comment: Accepted to ICASSP 202
Survival data analysis with time-dependent covariates using generalized additive models
We discuss a flexible method for modeling survival data using penalized smoothing splines when the values of covariates change for the duration of the study. The Cox proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. However, a number of theoretical problems with respect to the baseline survival function remain unsolved. We use the generalized additive models (GAMs) with B splines to estimate the survival function and select the optimum smoothing parameters based on a variant multifold cross-validation (CV) method. The methods are compared with the generalized cross-validation (GCV) method using data from a long-term study of patients with primary biliary cirrhosis (PBC)
Fault Detection, Diagnosis and Fault Tolerance Approaches in Dynamic Systems based on Black-Box Models
In this dissertation new contributions to the research area of fault detection and diagnosis in
dynamic systems are presented. The main research effort has been done on the development
of new on-line model-based fault detection and diagnosis (FDD) approaches based on blackbox
models (linear ARX models, and neural nonlinear ARX models). From a theoretical point
of view a white-box model is more desirable to perform the FDD tasks, but in most cases it is
very hard, or even impossible, to obtain. When the systems are complex, or difficult to model,
modelling based on black-box models is usually a good and often the only alternative. The
performance of the system identification methods plays a crucial role in the FDD methods
proposed.
Great research efforts have been made on the development of linear and nonlinear FDD
approaches to detect and diagnose multiplicative (parametric) faults, since most of the past
research work has been done focused on additive faults on sensors and actuators.
The main pre-requisites for the FDD methods developed are: a) the on-line application in a
real-time environment for systems under closed-loop control; b) the algorithms must be
implemented in discrete time, and the plants are systems in continuous time; c) a two or three
dimensional space for visualization and interpretation of the fault symptoms. An engineering
and pragmatic view of FDD approaches has been followed, and some new theoretical
contributions are presented in this dissertation.
The fault tolerance problem and the fault tolerant control (FTC) have been investigated, and
some ideas of the new FDD approaches have been incorporated in the FTC context.
One of the main ideas underlying the research done in this work is to detect and diagnose
faults occurring in continuous time systems via the analysis of the effect on the parameters of
the discrete time black-box ARX models or associated features. In the FDD methods
proposed, models for nominal operation and models for each faulty situation are constructed
in off-line operation, and used a posteriori in on-line operation.
The state of the art and some background concepts used for the research come from many
scientific areas. The main concepts related to data mining, multivariate statistics (principal
component analysis, PCA), linear and nonlinear dynamic systems, black-box models, system
identification, fault detection and diagnosis (FDD), pattern recognition and discriminant
analysis, and fault tolerant control (FTC), are briefly described. A sliding window version of
the principal components regression algorithm, termed SW-PCR, is proposed for parameter estimation. The sliding window parameter estimation algorithms are most appropriate for
fault detection and diagnosis than the recursive algorithms.
For linear SISO systems, a new fault detection and diagnosis approach based on dynamic
features (static gain and bandwidth) of ARX models is proposed, using a pattern classification
approach based on neural nonlinear discriminant analysis (NNLDA). A new approach for
fault detection (FDE) is proposed based on the application of the PCA method to the
parameter space of ARX models; this allows a dimensional reduction, and the definition of
thresholds based on multivariate statistics. This FDE method has been combined with a fault
diagnosis (FDG) method based on an influence matrix (IMX). This combined FDD method
(PCA & IMX) is suitable to deal with SISO or MIMO linear systems.
Most of the research on the fault detection and diagnosis area has been done for linear
systems. Few investigations exist in the FDD approaches for nonlinear systems. In this work,
two new nonlinear approaches to FDD are proposed that are appropriate to SISO or MISO
systems. A new architecture for a neural recurrent output predictor (NROP) is proposed,
incorporating an embedded neural parallel model, an external feedback and an adjustable gain
(design parameter). A new fault detection and diagnosis (FDD) approach for nonlinear
systems is proposed based on a bank of neural recurrent output predictors (NROPs). Each
neural NROP predictor is tuned to a specific fault. Also, a new FDD method based on the
application of neural nonlinear PCA to ARX model parameters is proposed, combined with a
pattern classification approach based on neural nonlinear discriminant analysis.
In order to evaluate the performance of the proposed FDD methodologies, many experiments
have been done using simulation models and a real setup. All the algorithms have been
developed in discrete time, except the process models. The process models considered for the
validation and tests of the FDD approaches are: a) a first order linear SISO system; b) a
second order SISO model of a DC motor; c) a MIMO system model, the three-tank
benchmark. A real nonlinear DC motor setup has also been used. A fault tolerant control
(FTC) approach has been proposed to solve the typical reconfiguration problem formulated
for the three-tank benchmark. This FTC approach incorporates the FDD method based on a
bank of NROP predictors, and on an adaptive optimal linear quadratic Gaussian controller