Computation of the one-dimensional unwrapped phase

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 101-102). "Cepstrum bibliography" (p. 67-100).In this thesis, the computation of the unwrapped phase of the discrete-time Fourier transform (DTFT) of a one-dimensional finite-length signal is explored. The phase of the DTFT is not unique, and may contain integer multiple of 27r discontinuities. The unwrapped phase is the instance of the phase function chosen to ensure continuity. This thesis presents existing algorithms for computing the unwrapped phase, discussing their weaknesses and strengths. Then two composite algorithms are proposed that use the existing ones, combining their strengths while avoiding their weaknesses. The core of the proposed methods is based on recent advances in polynomial factoring. The proposed methods are implemented and compared to the existing ones.by Zahi Nadim Karam.S.M

Precise Estimation of Vocal Tract and Voice Source Characteristics

This thesis addresses the problem of quality degradation in speech produced by parameter-based speech synthesis, within the framework of an articulatory-acoustic forward mapping. I first investigate current problems in speech parameterisation, and point out the fact that conventional parameterisation inaccurately extracts the vocal tract response due to interference from the harmonic structure of voiced speech. To overcome this problem, I introduce a method for estimating filter responses more precisely from periodic signals. The method achieves such estimation in the frequency domain by approximating all the harmonics observed in several frames based on a least squares criterion. It is shown that the proposed method is capable of estimating the response more accurately than widely-used frame-by-frame parameterisation, for simulations using synthetic speech and for an articulatory-acoustic mapping using actual speech. I also deal with the source-filter separation problem and independent control of the voice source characteristic during speech synthesis. I propose a statistical approach to separating out the vocal-tract filter response from the voice source characteristic using a large articulatory database. The approach realises such separation for voiced speech using an iterative approximation procedure under the assumption that the speech production process is a linear system composed of a voice source and a vocal-tract filter, and that each of the components is controlled independently by different sets of factors. Experimental results show that controlling the source characteristic greatly improves the accuracy of the articulatory-acoustic mapping, and that the spectral variation of the source characteristic is evidently influenced by the fundamental frequency or the power of speech. The thesis provides more accurate acoustical approximation of the vocal tract response, which will be beneficial in a wide range of speech technologies, and lays the groundwork in speech science for a new type of corpus-based statistical solution to the source-filter separation problem

Phase Space Analysis and Classification of Sonar Echoes in Shallow-Water Channels

A primary objective of active sonar systems is to detect, locate, and classify objects, such as mines, ships, and biologics, based on their sonar backscatter. A shallow-water ocean channel is a challenging environment in which to classify sonar echoes because interactions of the sonar signal with the ocean surface and bottom induce frequency-dependent changes (especially dispersion and damping) in the signal as it propagates, the effects of which typically grow with range. Accordingly, the observed signal depends not only on the initial target backscatter, but also the propagation channel and how far the signal has propagated. These propagation effects can increase the variability of observed target echoes and degrade classification performance. Furthermore, uncertainty of the exact propagation channel and random variations within a channel cause classification features extracted from the received sonar echo to behave as random variables.With the goal of improving sonar signal classification in shallow-water environments, this work develops a phase space framework for studying sound propagation in channels with dispersion and damping. This approach leads to new moment features for classification that are invariant to dispersion and damping, the utility of which is demonstrated via simulation. In addition, the accuracy of a previously developed phase space approximation method for range-independent pulse propagation is analyzed and shown to be greater than the accuracy of the standard stationary phase approximation for both large and small times/distances. The phase space approximation is also extended to range dependent propagation. Finally, the phase space approximation is used to investigate the random nature of moment features for classification by calculating the moments of the moment features under uncertain and random channel assumptions. These moments of the moment features are used to estimate probability distribution functions for the moment features, and we explore several ways in which this information may be used to improve sonar classification performance

Analysis and correction of the helium speech effect by autoregressive signal processing

SIGLELD:D48902/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Automatic Speech Emotion Recognition- Feature Space Dimensionality and Classification Challenges

In the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SER and examine the process of designing automatic emotion recognition schemes. There are major challenges to automatic SER including ambiguity about the list/definition of emotions, the lack of agreement on a manageable set of uncorrelated speech-based emotion relevant features, and the difficulty of collected emotion-related datasets under natural circumstances. We shall initiate our work by dealing with the identification of appropriate sets of emotion related features/attributes extractible from speech signals as considered from psychological and computational points of views. We shall investigate the use of pattern-recognition approaches to remove redundancies and achieve compactification of digital representation of the extracted data with minimal loss of information. The thesis will include the design of new or complement existing SER schemes and conduct large sets of experiments to empirically test their performances on different databases, identify advantages, and shortcomings of using speech alone for emotion recognition. Existing SER studies seem to deal with the ambiguity/dis-agreement on a “limited” number of emotion-related features by expanding the list from the same speech signal source/sites and apply various feature selection procedures as a mean of reducing redundancies. Attempts are made to discover more relevant features to emotion from speech. One of our investigations focuses on proposing a newly sets of features for SER, extracted from Linear Predictive (LP)-residual speech. We shall demonstrate the usefulness of the proposed relatively small set of features by testing the performance of an SER scheme that is based on fusing our set of features with the existing set of thousands of features using common machine learning schemes of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The challenge of growing dimensionality of SER feature space and its impact on increased model complexity is another major focus of our research project. By studying the pros and cons of the commonly used feature selection approaches, we argued in favour of meta-feature selection and developed various methods in this direction, not only to reduce dimension, but also to adapt and de-correlate emotional feature spaces for improved SER model recognition accuracy. We used rincipal Component Analysis (PCA) and proposed Data Independent PCA (DIPCA) by training on independent emotional and non-emotional datasets. The DIPCA projections, especially when extracted from speech data coloured with different emotions or from Neutral speech data, had comparable capability to the PCA in terms of SER performance. Another adopted approach in this thesis for dimension reduction is the Random Projection (RP) matrices, independent of training data. We have shown that some versions of RP with SVM classifier can offer an adaptation space for Speaker Independent SER that avoid over-fitting and hence improves recognition accuracy. Using PCA trained on a set of data, while testing on emotional data features, has significant implication for machine learning in general. The thesis other major contribution focuses on the classification aspects of SER. We investigate the drawbacks of the well-known SVM classifier when applied to a preprocessed data by PCA and RP. We shall demonstrate the advantages of using the Linear Discriminant Classifier (LDC) instead especially for PCA de-correlated metafeatures. We initiated a variety of LDC-based ensembles classification, to test performance of scheme using a new form of bagging different subsets of metafeature subsets extracted by PCA with encouraging results. The experiments conducted were applied on two benchmark datasets (Emo-Berlin and FAU-Aibo), and an in-house dataset in the Kurdish language. Recognition accuracy achieved by are significantly higher than the state of art results on all datasets. The results, however, revealed a difficult challenge in the form of persisting wide gap in accuracy over different datasets, which cannot be explained entirely by the differences between the natures of the datasets. We conducted various pilot studies that were based on various visualizations of the confusion matrices for the “difficult” databases to build multi-level SER schemes. These studies provide initial evidences to the presence of more than one “emotion” in the same portion of speech. A possible solution may be through presenting recognition accuracy in a score-based measurement like the spider chart. Such an approach may also reveal the presence of Doddington zoo phenomena in SER

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Design of hardware architectures for HMM–based signal processing systems with applications to advanced human-machine interfaces

In questa tesi viene proposto un nuovo approccio per lo sviluppo di interfacce uomo–macchina. In particolare si tratta il caso di sistemi di pattern recognition che fanno uso di Hidden Markov Models per la classificazione. Il progetto di ricerca è partito dall’ideazione di nuove tecniche per la realizzazione di sistemi di riconoscimento vocale per parlato spontaneo. Gli HMM sono stati scelti come lo strumento algoritmico di base per la realizzazione del sistema. Dopo una fase di studio preliminare gli obiettivi sono stati estesi alla realizzazione di una architettura hardware in grado di fornire uno strumento riconfigurabile che possa essere utilizzato non solo per il riconoscimento vocale, ma in qualsiasi tipo di classificatore basato su HMM. Il lavoro si concentra quindi sullo sviluppo di architetture hardware dedicate, ma nuovi risultati sono stati ottenuti anche a livello di applicazione per quanto riguarda la classificazione di segnali elettroencefalografici attraverso gli HMM. Innanzitutto state sviluppata una architettura a livello di sistema applicabile a qualsiasi sistema di pattern recognition che faccia usi di HMM. L’architettura stata concepita in modo tale da essere utilizzabile come un sistema stand–alone. Definita l’architettura, un processore hardware per HMM, completamente riconfigurabile, stato decritto in linguaggio VHDL e simulato con successo. Un array parallelo di questi processori costituisce di fatto il nucleo di processamento dell’architettura sviluppata. Sulla base del progetto in VHDL, due piattaforme di prototipaggio rapido basate su FPGA sono state selezionate per dei test di implementazione. Diverse configurazioni costituite da array paralleli di processori HMM sono state implementate su FPGA. Le soluzioni che offrivano un miglior compromesso tra prestazioni e quantità di risorse hardware utilizzate sono state selezionate per ulteriori analisi. Un sistema software per il pattern recognition basato su HMM stato scelto come sistema di riferimento per verificare la corretta funzionalità delle architetture implementate. Diversi test sono stati progettati per validare che il funzionamento del sistema corrispondesse alle specifiche iniziali. Le versioni implementate del sistema sono state confrontate con il software di riferimento sulla base dei risultati forniti dai test. Dal confronto è stato possibile appurare che le architetture sviluppate hanno un comportamento corrispondente a quello richiesto. Infine le implementazioni dell’array parallelo di processori HMM `e sono state applicate a due applicazioni reali: un riconoscitore vocale, ed un classificatore per interfacce basate su segnali elettroencefalografici. In entrambi i casi l’architettura si è dimostrata in grado di gestire l’applicazione senza alcun problema. L’uso del processamento hardware per il riconoscimento vocale apre di fatto la strada a nuovi sviluppi nel campo grazie al notevole incremento di prestazioni ottenibili in termini di tempo di esecuzione. L’applicazione al processamento dell’EEG, invece, introduce di fatto un approccio completamente nuovo alla classificazione di questo tipo di segnali, e mostra come in futuro potrebbe essere possibile lo sviluppo di interfacce basate sulla classificazione dei segnali generati dal pensiero spontaneo. I possibili sviluppi del lavoro iniziato con questa tesi sono molteplici. Una direzione possibile è quella dell’implementazione completa dell’architettura proposta come un sistema stand–alone riconfigurabile per l’accelerazione di sistemi per pattern recognition di qualsiasi natura purchè basati su HMM. Le potenzialità di tale sistema renderebbero possibile la realizzazione di classificatiori in tempo reale con un alto grado di complessità, e quindi allo sviluppo di interfacce realmente multimodali, con una vasta gamma di applicazioni, dai sistemi di per lo spazio a quelli di supporto per persone disabili.In this thesis a new approach is described for the development of human–computer interfaces. In particular the case of pattern recognition systems based on Hidden Markov Models have been taken into account. The research started from he development of techniques for the realization of natural language speech recognition systems. The Hidden Markov Model (HMM) was chosen as the main algorithmic tool to be used to build the system. After the early work the goal was extended to the development of an hardware architecture that provided a reconfigurable tool to be used in any pattern recognition task, and not only in speech recognition. The whole work is thus focused on the development of dedicated hardware architectures, but also some new results have been obtained on the classification of electroencephalographic signals through the use of HMMs. Firstly a system–level architecture has been developed to be used in HMM based pattern recognition systems. The architecture has been conceived in order to be able to work as a stand–alone system. Then a VHDL description has been made of a flexible and completely reconfigurable hardware HMM processor and the design was successfully simulated. A parallel array of these processors is actually the core processing block of the developed architecture. Then two suitable FPGA based, fast prototyping platforms have been identified to be the targets for the implementation tests. Different configurations of parallel HMM processor arrays have been set up and mapped on the target FPGAs. Some solutions have been selected to be the best in terms of balance between performance and resources utilization. Furthermore a software HMM based pattern recognition system has been chosen to be the reference system for the functionality of the implemented subsystems. A set of tests have been developed with the aim to test the correct functionality of the hardware. The implemented system was compared to the reference system on the basis of the tests’ results, and it was found that the behavior was the one expected and the required functionality was correctly achieved. Finally the implementation of the parallel HMM array was tested through its application to two real–world applications: a speech recognition task and a brain–computer interface task. In both cases the architecture showed to be functionally suitable and powerful enough to handle the task without problems. The application of the hardware processing to speech recognition opens new perspectives in the design of this kind of systems because of the dramatic increment in performance. The application to brain–computer interface is really interesting because of a new approach in the classification of EEG that shows how could be possible a future development of interfaces based on the classification of spontaneous thought. The possible evolution directions of the work started with this thesis are many. Effort could be spent of the implementation of the developed architecture as a stand–alone reconfigurable system suitable for any kind of HMM–based pattern recognition task. The potential performance of such a system could open the way to extremely complex real–time pattern recognition systems, and thus to the realization of truly multimodal interfaces, with a variety of applications, from space to aid systems for the impaired