Parameter Estimation -in sparsity we trust

This thesis is based on nine papers, all concerned with parameter estimation. The thesis aims at solving problems related to real-world applications such as spectroscopy, DNA sequencing, and audio processing, using sparse modeling heuristics. For the problems considered in this thesis, one is not only concerned with finding the parameters in the signal model, but also to determine the number of signal components present in the measurements. In recent years, developments in sparse modeling have allowed for methods that jointly estimate the parameters in the model and the model order. Based on these achievements, the approach often taken in this thesis is as follows. First, a parametric model of the considered signal is derived, containing different parameters that capture the important characteristics of the signal. When the signal model has been determined, an optimization problem is formed aimed at finding ...

Swärd, Johan — Lund University


Probabilistic Model-Based Multiple Pitch Tracking of Speech

Multiple pitch tracking of speech is an important task for the segregation of multiple speakers in a single-channel recording. In this thesis, a probabilistic model-based approach for estimation and tracking of multiple pitch trajectories is proposed. A probabilistic model that captures pitch-dependent characteristics of the single-speaker short-time spectrum is obtained a priori from clean speech data. The resulting speaker model, which is based on Gaussian mixture models, can be trained either in a speaker independent (SI) or a speaker dependent (SD) fashion. Speaker models are then combined using an interaction model to obtain a probabilistic description of the observed speech mixture. A factorial hidden Markov model is applied for tracking the pitch trajectories of multiple speakers over time. The probabilistic model-based approach is capable to explicitly incorporate timbral information and all associated uncertainties of spectral structure into the model. While ...

Wohlmayr, Michael — Graz University of Technology


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


Sparse Modeling Heuristics for Parameter Estimation - Applications in Statistical Signal Processing

This thesis examines sparse statistical modeling on a range of applications in audio modeling, audio localizations, DNA sequencing, and spectroscopy. In the examined cases, the resulting estimation problems are computationally cumbersome, both as one often suffers from a lack of model order knowledge for this form of problems, but also due to the high dimensionality of the parameter spaces, which typically also yield optimization problems with numerous local minima. In this thesis, these problems are treated using sparse modeling heuristics, with the resulting criteria being solved using convex relaxations, inspired from disciplined convex programming ideas, to maintain tractability. The contributions to audio modeling and estimation focus on the estimation of the fundamental frequency of harmonically related sinusoidal signals, which is commonly used model for, e.g., voiced speech or tonal audio. We examine both the problems of estimating multiple audio sources ...

Adalbjörnsson, Stefan Ingi — Lund University


Parallel Magnetic Resonance Imaging reconstruction problems using wavelet representations

To reduce scanning time or improve spatio-temporal resolution in some MRI applications, parallel MRI acquisition techniques with multiple coils have emerged since the early 90’s as powerful methods. In these techniques, MRI images have to be reconstructed from ac- quired undersampled “k-space” data. To this end, several reconstruction techniques have been proposed such as the widely-used SENSitivity Encoding (SENSE) method. However, the reconstructed images generally present artifacts due to the noise corrupting the ob- served data and coil sensitivity profile estimation errors. In this work, we present novel SENSE-based reconstruction methods which proceed with regularization in the complex wavelet domain so as to promote the sparsity of the solution. These methods achieve ac- curate image reconstruction under degraded experimental conditions, in which neither the SENSE method nor standard regularized methods (e.g. Tikhonov) give convincing results. The proposed approaches relies on ...

Lotfi CHAARI — University Paris-Est


A Computational Framework for Sound Segregation in Music Signals

Music is built from sound, ultimately resulting from an elaborate interaction between the sound-generating properties of physical objects (i.e. music instruments) and the sound perception abilities of the human auditory system. Humans, even without any kind of formal music training, are typically able to ex- tract, almost unconsciously, a great amount of relevant information from a musical signal. Features such as the beat of a musical piece, the main melody of a complex musical ar- rangement, the sound sources and events occurring in a complex musical mixture, the song structure (e.g. verse, chorus, bridge) and the musical genre of a piece, are just some examples of the level of knowledge that a naive listener is commonly able to extract just from listening to a musical piece. In order to do so, the human auditory system uses a variety of cues ...

Martins, Luis Gustavo — Universidade do Porto


Adaptive Nonlocal Signal Restoration and Enhancement Techniques for High-Dimensional Data

The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Different applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require different sensing technologies to form the image. In all cases, even with the advance of ...

Maggioni, Matteo — Tampere University of Technology


Enhancement of Speech Signals - with a Focus on Voiced Speech Models

The topic of this thesis is speech enhancement with a focus on models of voiced speech. Speech is divided into two subcategories dependent on the characteristics of the signal. One part is the voiced speech, the other is the unvoiced. In this thesis, we primarily focus on the voiced speech parts and utilise the structure of the signal in relation to speech enhancement. The basis for the models is the harmonic model which is a very often used model for voiced speech because it describes periodic signals perfectly. First, we consider the problem of non-stationarity in the speech signal. The speech signal changes its characteristics continuously over time whereas most speech analysis and enhancement methods assume stationarity within 20-30 ms. We propose to change the model to allow the fundamental frequency to vary linearly over time by introducing a chirp ...

Nørholm, Sidsel Marie — Aalborg University


Sparse approximation and dictionary learning with applications to audio signals

Over-complete transforms have recently become the focus of a wide wealth of research in signal processing, machine learning, statistics and related fields. Their great modelling flexibility allows to find sparse representations and approximations of data that in turn prove to be very efficient in a wide range of applications. Sparse models express signals as linear combinations of a few basis functions called atoms taken from a so-called dictionary. Finding the optimal dictionary from a set of training signals of a given class is the objective of dictionary learning and the main focus of this thesis. The experimental evidence presented here focuses on the processing of audio signals, and the role of sparse algorithms in audio applications is accordingly highlighted. The first main contribution of this thesis is the development of a pitch-synchronous transform where the frame-by-frame analysis of audio data ...

Barchiesi, Daniele — Queen Mary University of London


Parameter Estimation and Filtering Using Sparse Modeling

Sparsity-based estimation techniques deal with the problem of retrieving a data vector from an undercomplete set of linear observations, when the data vector is known to have few nonzero elements with unknown positions. It is also known as the atomic decomposition problem, and has been carefully studied in the field of compressed sensing. Recent findings have led to a method called basis pursuit, also known as Least Absolute Shrinkage and Selection Operator (LASSO), as a numerically reliable sparsity-based approach. Although the atomic decomposition problem is generally NP-hard, it has been shown that basis pursuit may provide exact solutions under certain assumptions. This has led to an extensive study of signals with sparse representation in different domains, providing a new general insight into signal processing. This thesis further investigates the role of sparsity-based techniques, especially basis pursuit, for solving parameter estimation ...

Panahi, Ashkan — Chalmers University of Technology


Discrete-time speech processing with application to emotion recognition

The subject of this PhD thesis is the efficient and robust processing and analysis of the audio recordings that are derived from a call center. The thesis is comprised of two parts. The first part is dedicated to dialogue/non-dialogue detection and to speaker segmentation. The systems that are developed are prerequisite for detecting (i) the audio segments that actually contain a dialogue between the system and the call center customer and (ii) the change points between the system and the customer. This way the volume of the audio recordings that need to be processed is significantly reduced, while the system is automated. To detect the presence of a dialogue several systems are developed. This is the first effort found in the international literature that the audio channel is exclusively exploited. Also, it is the first time that the speaker utterance ...

Kotti, Margarita — Aristotle University of Thessaloniki


Adaptive filtering algorithms for acoustic echo cancellation and acoustic feedback control in speech communication applications

Multimedia consumer electronics are nowadays everywhere from teleconferencing, hands-free communications, in-car communications to smart TV applications and more. We are living in a world of telecommunication where ideal scenarios for implementing these applications are hard to find. Instead, practical implementations typically bring many problems associated to each real-life scenario. This thesis mainly focuses on two of these problems, namely, acoustic echo and acoustic feedback. On the one hand, acoustic echo cancellation (AEC) is widely used in mobile and hands-free telephony where the existence of echoes degrades the intelligibility and listening comfort. On the other hand, acoustic feedback limits the maximum amplification that can be applied in, e.g., in-car communications or in conferencing systems, before howling due to instability, appears. Even though AEC and acoustic feedback cancellation (AFC) are functional in many applications, there are still open issues. This means that ...

Gil-Cacho, Jose Manuel — KU Leuven


Energy-Efficient Distributed Multicast Beamforming Using Iterative Second-Order Cone Programming

In multi-user (MU) downlink beamforming, a high spectral efficiency along with a low transmit power is achieved by separating multiple users in space rather than in time or frequency using spatially selective transmit beams. For streaming media applications, multi-group multicast (MGM) downlink beamforming is a promising approach to exploit the broadcasting property of the wireless medium to transmit the same information to a group of users. To limit inter-group interference, the individual streams intended for different multicast groups are spatially separated using MGM downlink beamforming. Spatially selective downlink beamforming requires the employment of an array of multiple antennas at the base station (BS). The hardware costs associated with the use of multiple antennas may be prohibitive in practice. A way to avoid the expensive employment of multiple antennas at the BS is to exploit user cooperation in wireless networks where ...

Bornhorst, Nils — Technische Universität Darmstadt


Regularization techniques in model fitting and parameter estimation

We consider fitting data by linear and nonlinear models. The specific problems that we aim at, although they encompass classic formulations, have as common ground the fact that we attack a special situation: the ill-posed problems. In the linear case, we consider the total least squares problem. There exist special methods to approach the so-called nongeneric cases, but we propose extensions for the more commonly encountered close-to-nongeneric problems. Several methods of introducing regularization in the context of total least squares are analyzed. They are based on truncation methods or on penalty optimization. The obtained problems might not have closed form solutions. We discuss numerical linear algebra and local optimization methods. Data fitting by nonlinear or nonparametric models is the second subject of the thesis. We extend the nonlinear regression theory to the case when we have to deal with supplementary ...

Sima, Diana — Katholieke Universiteit Leuven


Bayesian Fusion of Multi-band Images: A Powerful Tool for Super-resolution

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne ...

Wei, Qi — University of Toulouse

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