Modeling and Sampling of Spectrally Structured Signals
Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)
Abstract
This thesis consists of five papers concerned with the modeling of stochastic signals, as well as deterministic signals in stochastic noise, exhibiting different kinds of structure. This structure is manifested as the existence of finitedimensional parameterizations, and/or in the geometry of the signals' spectral representations.
The two first papers of the thesis, Papers A and B, consider the modeling of differences, or distances, between stochastic processes based on their secondorder statistics, i.e., covariances. By relating the covariance structure of a stochastic process to spectral representations, it is proposed to quantify the dissimilarity between two processes in terms of the cost associated with morphing one spectral representation to the other, with the cost of morphing being defined in terms of the solutions to optimal mass transport problems. The proposed framework allows for modeling smooth changes in the frequency characteristics of stochastic processes, which is shown to yield interpretable and physically sensible predictions when used in applications of temporal and spatial spectral estimation. Also presented are efficient computational tools, allowing for the framework to be used in highdimensional problems.
Paper C considers the modeling of socalled inharmonic signals, i.e., signals that are almost, but not quite, harmonic. Such signals appear in many fields of signal processing, not least in audio. Inharmonicity may be interpreted as a deviation from a parametric structure, as well as from a particular spectral structure. Based on these views, as well as on a third, stochastic interpretation, Paper C proposes three different definitions of the concept of fundamental frequency for inharmonic signals, and studies the estimation theoretical implications of utilizing either of these definitions. Paper D then considers deliberate deviations from a parametric signal structure arising in spectroscopy applications. With the motivation of decreasing the computational complexity of parameter estimation, the paper studies the implications of estimating the parameters of the signal in a sequential fashion, starting out with a simplified model that is then refined step by step.
Lastly, Paper E studies how parametric descriptions of signals can be leveraged as to design optimal, in an estimation theoretical sense, schemes for sampling or collecting measurements from the signal. By means of a convex program, samples are selected as to minimize bounds on estimator variance, allowing for efficiently measuring parametric signals, even when the parametrization is only partially known.
The two first papers of the thesis, Papers A and B, consider the modeling of differences, or distances, between stochastic processes based on their secondorder statistics, i.e., covariances. By relating the covariance structure of a stochastic process to spectral representations, it is proposed to quantify the dissimilarity between two processes in terms of the cost associated with morphing one spectral representation to the other, with the cost of morphing being defined in terms of the solutions to optimal mass transport problems. The proposed framework allows for modeling smooth changes in the frequency characteristics of stochastic processes, which is shown to yield interpretable and physically sensible predictions when used in applications of temporal and spatial spectral estimation. Also presented are efficient computational tools, allowing for the framework to be used in highdimensional problems.
Paper C considers the modeling of socalled inharmonic signals, i.e., signals that are almost, but not quite, harmonic. Such signals appear in many fields of signal processing, not least in audio. Inharmonicity may be interpreted as a deviation from a parametric structure, as well as from a particular spectral structure. Based on these views, as well as on a third, stochastic interpretation, Paper C proposes three different definitions of the concept of fundamental frequency for inharmonic signals, and studies the estimation theoretical implications of utilizing either of these definitions. Paper D then considers deliberate deviations from a parametric signal structure arising in spectroscopy applications. With the motivation of decreasing the computational complexity of parameter estimation, the paper studies the implications of estimating the parameters of the signal in a sequential fashion, starting out with a simplified model that is then refined step by step.
Lastly, Paper E studies how parametric descriptions of signals can be leveraged as to design optimal, in an estimation theoretical sense, schemes for sampling or collecting measurements from the signal. By means of a convex program, samples are selected as to minimize bounds on estimator variance, allowing for efficiently measuring parametric signals, even when the parametrization is only partially known.
Detaljer
Författare  

Enheter & grupper  
Forskningsområden  Ämnesklassifikation (UKÄ) – OBLIGATORISK
Nyckelord 
Originalspråk  engelska 

Kvalifikation  Doktor 
Tilldelande institution  
Handledare/Biträdande handledare 

Tilldelningsdatum  2020 jun 12 
Förlag 

Tryckta ISBN  9789178954889 
Elektroniska ISBN  9789178954896 
Status  Published  2020 
Publikationskategori  Forskning 
Nedladdningar
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Relaterad forskningsoutput
Filip Elvander, Haasler, I., Andreas Jakobsson & Karlsson, J., 2020, I : Signal Processing. 171, 107474.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift
Filip Elvander, Johan Sward & Andreas Jakobsson, 2019, 2019 IEEE 8th International Workshop on Computational Advances in MultiSensor Adaptive Processing, CAMSAP 2019  Proceedings. Institute of Electrical and Electronics Engineers Inc., s. 246250 5 s. 9022668. (2019 IEEE 8th International Workshop on Computational Advances in MultiSensor Adaptive Processing, CAMSAP 2019  Proceedings).
Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding
Johan Swärd, Filip Elvander & Andreas Jakobsson, 2018, I : Signal Processing. 150, s. 110 10 s.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift