Nonlinear analysis of speech from a synthesis perspective

With the emergence of nonlinear dynamical systems analysis over recent years it has become clear that conventional time domain and frequency domain approaches to speech synthesis may be far from optimal. Using state space reconstructions of the time domain speech signal it is, at least in theory, possible to investigate a number of invariant geometrical measures for the underlying system which give a more thorough understanding of the dynamics of the system and therefore the form that any model should take. This thesis introduces a number of nonlinear dynamical analysis tools which are then applied to a database of vowels to extract the underlying invariant geometrical properties. The results of this analysis are then applied, using ideas taken from nonlinear dynamics, to the problem of speech synthesis and a novel synthesis technique is described and demonstrated. The tools used for ...

Banbrook, Mike — University Of Edinburgh


An Investigation of Nonlinear Speech Synthesis and Pitch Modification Techniques

Speech synthesis technology plays an important role in many aspects of man–machine interaction, particularly in telephony applications. In order to be widely accepted, the synthesised speech quality should be as human–like as possible. This thesis investigates novel techniques for the speech signal generation stage in a speech synthesiser, based on concepts from nonlinear dynamical theory. It focuses on natural–sounding synthesis for voiced speech, coupled with the ability to generate the sound at the required pitch. The one–dimensional voiced speech time–domain signals are embedded into an appropriate higher dimensional space, using Takens’ method of delays. These reconstructed state space representations have approximately the same dynamical properties as the original speech generating system and are thus effective models. A new technique for marking epoch points in voiced speech that operates in the state space domain is proposed. Using the fact that one ...

Mann, Iain — University Of Edinburgh


Nonlinear Noise Cancellation

Noise or interference is often assumed to be a random process. Conventional linear filtering, control or prediction techniques are used to cancel or reduce the noise. However, some noise processes have been shown to be nonlinear and deterministic. These nonlinear deterministic noise processes appear to be random when analysed with second order statistics. As nonlinear processes are widespread in nature it may be beneficial to exploit the coherence of the nonlinear deterministic noise with nonlinear filtering techniques. The nonlinear deterministic noise processes used in this thesis are generated from nonlinear difference or differential equations which are derived from real world scenarios. Analysis tools from the theory of nonlinear dynamics are used to determine an appropriate sampling rate of the nonlinear deterministic noise processes and their embedding dimensions. Nonlinear models, such as the Volterra series filter and the radial basis function ...

Strauch, Paul E. — University Of Edinburgh


Flexible Multi-Microphone Acquisition and Processing of Spatial Sound Using Parametric Sound Field Representations

This thesis deals with the efficient and flexible acquisition and processing of spatial sound using multiple microphones. In spatial sound acquisition and processing, we use multiple microphones to capture the sound of multiple sources being simultaneously active at a rever- berant recording side and process the sound depending on the application at the application side. Typical applications include source extraction, immersive spatial sound reproduction, or speech enhancement. A flexible sound acquisition and processing means that we can capture the sound with almost arbitrary microphone configurations without constraining the application at the ap- plication side. This means that we can realize and adjust the different applications indepen- dently of the microphone configuration used at the recording side. For example in spatial sound reproduction, where we aim at reproducing the sound such that the listener perceives the same impression as if he ...

Thiergart, Oliver — Friedrich-Alexander-Universitat Erlangen-Nurnberg


Speech derereverberation in noisy environments using time-frequency domain signal models

Reverberation is the sum of reflected sound waves and is present in any conventional room. Speech communication devices such as mobile phones in hands-free mode, tablets, smart TVs, teleconferencing systems, hearing aids, voice-controlled systems, etc. use one or more microphones to pick up the desired speech signals. When the microphones are not in the proximity of the desired source, strong reverberation and noise can degrade the signal quality at the microphones and can impair the intelligibility and the performance of automatic speech recognizers. Therefore, it is a highly demanded task to process the microphone signals such that reverberation and noise are reduced. The process of reducing or removing reverberation from recorded signals is called dereverberation. As dereverberation is usually a completely blind problem, where the only available information are the microphone signals, and as the acoustic scenario can be non-stationary, ...

Braun, Sebastian — Friedrich-Alexander Universität Erlangen-Nürnberg


Synthetic reproduction of head-related transfer functions by using microphone arrays

Spatial hearing for human listeners is based on the interaural as well as on the monaural analysis of the signals arriving at both ears, enabling the listeners to assign certain spatial components to these signals. This spatial aspect gets lost when the signals are reproduced via headphones without considering the acoustical influence of the head and torso, i.e. head-related transfer function (HRTFs). A common procedure to take into account spatial aspects in a binaural reproduction is to use so-called artificial heads. Artificial heads are replicas of a human head and torso with average anthropometric geometries and built-in microphones in the ears. Although, the signals recorded with artificial heads contain relevant spatial aspects, binaural recordings using artificial heads often suffer from front-back confusions and the perception of the sound source being inside the head (internalization). These shortcomings can be attributed to ...

Rasumow, Eugen — University of Oldenburg


Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path ...

Comminiello, Danilo — Sapienza University of Rome


Nonlinear processing of non-Gaussian stochastic and chaotic deterministic time series

It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian noise models are appealing as they usually result in noise suppression algorithms that are simple: i.e. linear and closed form. However, such linear techniques may be sub-optimal when the noise process is either a non-Gaussian stochastic process or a chaotic deterministic process. In the event of encountering such noise processes, improvements in noise suppression, relative to the performance of linear methods, may be achievable using nonlinear signal processing techniques. The application of interest for this thesis is maritime surveillance radar, where the main source of interference, termed sea clutter, is widely accepted to be a non-Gaussian stochastic process at high resolutions and/or at low grazing angles. However, evidence has been presented during the last decade which suggests that sea clutter may be better modelled as a ...

Cowper, Mark — University Of Edinburgh


Statistical Parametric Speech Synthesis Based on the Degree of Articulation

Nowadays, speech synthesis is part of various daily life applications. The ultimate goal of such technologies consists in extending the possibilities of interaction with the machine, in order to get closer to human-like communications. However, current state-of-the-art systems often lack of realism: although high-quality speech synthesis can be produced by many researchers and companies around the world, synthetic voices are generally perceived as hyperarticulated. In any case, their degree of articulation is fixed once and for all. The present thesis falls within the more general quest for enriching expressivity in speech synthesis. The main idea consists in improving statistical parametric speech synthesis, whose most famous example is Hidden Markov Model (HMM) based speech synthesis, by introducing a control of the articulation degree, so as to enable synthesizers to automatically adapt their way of speaking to the contextual situation, like humans ...

Picart, Benjamin — Université de Mons (UMONS)


Solving inverse problems in room acoustics using physical models, sparse regularization and numerical optimization

Reverberation consists of a complex acoustic phenomenon that occurs inside rooms. Many audio signal processing methods, addressing source localization, signal enhancement and other tasks, often assume absence of reverberation. Consequently, reverberant environments are considered challenging as state-ofthe-art methods can perform poorly. The acoustics of a room can be described using a variety of mathematical models, among which, physical models are the most complete and accurate. The use of physical models in audio signal processing methods is often non-trivial since it can lead to ill-posed inverse problems. These inverse problems require proper regularization to achieve meaningful results and involve the solution of computationally intensive large-scale optimization problems. Recently, however, sparse regularization has been applied successfully to inverse problems arising in different scientific areas. The increased computational power of modern computers and the development of new efficient optimization algorithms makes it possible ...

Antonello, Niccolò — KU Leuven


The use of High-Order Sparse Linear Prediction for the Restoration of Archived Audio

Since the invention of Gramophone by Thomas Edison in 1877, vast amounts of cultural, entertainment, educational and historical audio recordings have been recorded and stored throughout the world. Through natural aging and improper storage, the recorded signal degrades and loses its information in terms of quality and intelligibility. Degradation of audio signals is considered as any unwanted modification to the audio signal after it has been recorded. There are different degradations affecting recorded signals on analog storage media. The degradations that are often encountered are clicks, hiss and ‘Wow and Flutter’. Several researches have been conducted in restoring degraded audio recordings. Most of the methods rely on some prior information of the underlying data and the degradation process. The success of these methods heavily depends on the prior information available. When such information is not available, a model of the ...

Dufera, Bisrat Derebssa — School of Electrical and Computer Engineering, Addis Ababa Institute of Technology, Addis Ababa University


Transformation methods in signal processing

This dissertation is concerned with the application of the theory of rational functions in signal processing. The PhD thesis summarizes the corresponding results of the author’s research. Since the systems of rational functions are defined by the collection of inverse poles with multiplicities, the following parameters should be determined: the number, the positions and the multiplicities of the inverse poles. Therefore, we develop the hyperbolic variant of the so-called Nelder–Mead and the particle swarm optimization algorithm. In addition, the latter one is integrated into a more general multi-dimensional framework. Furthermore, we perform a detailed stability and error analysis of these methods. We propose an electrocardiogram signal generator based on spline interpolation. It turns to be an efficient tool for testing and evaluating signal models, filtering techniques, etc. In this thesis, the synthesized heartbeats are used to test the diagnostic distortion ...

Kovács, Péter — Eötvös L. University, Budapest, Hungary


A Multimodal Approach to Audiovisual Text-to-Speech Synthesis

Speech, consisting of an auditory and a visual signal, has always been the most important means of communication between humans. It is well known that an optimal conveyance of the message requires that both the auditory and the visual speech signal can be perceived by the receiver. Nowadays people interact countless times with computer systems in every-day situations. Since the ultimate goal is to make this interaction feel completely natural and familiar, the most optimal way to interact with a computer system is by means of speech. Similar to the speech communication between humans, the most appropriate human-machine interaction consists of audiovisual speech signals. In order to allow the computer system to transfer a spoken message towards its users, an audiovisual speech synthesizer is needed to generate novel audiovisual speech signals based on a given text. This dissertation focuses on ...

Mattheyses, Wesley — Vrije Universiteit Brussel


Motion Analysis and Modeling for Activity Recognition and 3-D Animation based on Geometrical and Video Processing Algorithms

The analysis of audiovisual data aims at extracting high level information, equivalent with the one(s) that can be extracted by a human. It is considered as a fundamental, unsolved (in its general form) problem. Even though the inverse problem, the audiovisual (sound and animation) synthesis, is judged easier than the previous, it remains an unsolved problem. The systematic research on these problems yields solutions that constitute the basis for a great number of continuously developing applications. In this thesis, we examine the two aforementioned fundamental problems. We propose algorithms and models of analysis and synthesis of articulated motion and undulatory (snake) locomotion, using data from video sequences. The goal of this research is the multilevel information extraction from video, like object tracking and activity recognition, and the 3-D animation synthesis in virtual environments based on the results of analysis. An ...

Panagiotakis, Costas — University of Crete


Sparsity in Linear Predictive Coding of Speech

This thesis deals with developing improved modeling methods for speech and audio processing based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the well-known linear prediction (LP) based all-pole models currently applied in many modern speech and audio processing systems. In the first part of this thesis, we introduce \emph{Sparse Linear Prediction}, a set of speech processing tools created by introducing sparsity constraints into the LP framework. This approach defines predictors that look for a sparse residual rather than a minimum variance one, with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter is model as an impulse train. Introducing sparsity in the LP framework, will also bring to develop the ...

Giacobello, Daniele — Aalborg University

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