Sökning: "Statistical Signal Processing Group"

Visar resultat 1 - 5 av 14 avhandlingar innehållade orden Statistical Signal Processing Group.

  1. 1. Group-Sparse Regression : With Applications in Spectral Analysis and Audio Signal Processing

    Författare :Ted Kronvall; Statistical Signal Processing Group; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; sparse regression; group-sparsity; statistical modeling; regularization; hyperparameter-selection; spectral analysis; audio signal processing; classification; localization; multi-pitch estimation; chroma; convex optimization; ADMM; cyclic coordinate descent; proximal gradient;

    Sammanfattning : This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e. LÄS MER

  2. 2. Statistical inference and time-frequency estimation for non-stationary signal classification

    Författare :Rachele Anderson; Statistical Signal Processing Group; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; Non-stationary processes; stochastic modeling; inference; spectral analysis; time-frequency analysis; classification; biomedical applications; deep learning;

    Sammanfattning : This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. LÄS MER

  3. 3. Sparse Modeling of Harmonic Signals

    Författare :Filip Elvander; Statistical Signal Processing Group; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; multi-pitch estimation; sparse modeling; convex optimisation; inharmonicity; sparse recursive least squares; adaptive signal processing; optimal transport distance;

    Sammanfattning : This thesis considers sparse modeling and estimation of multi-pitch signals, i.e., signals whose frequency content can be described by superpositions of harmonic, or close-to-harmonic, structures, characterized by a set of fundamental frequencies. LÄS MER

  4. 4. High resolution time-frequency representations

    Författare :Isabella Reinhold; Statistical Signal Processing Group; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; time-frequency analysis; non-stationary signals; multi-component signals; IF estimation; reassignment;

    Sammanfattning : Non-stationary signals are very common in nature, e.g. sound waves such as human speech, bird song and music. It is usually meaningful to describe a signal in terms of time and frequency. LÄS MER

  5. 5. Parameter Estimation - in sparsity we trust

    Författare :Johan Swärd; Statistical Signal Processing Group; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; Parameter estimation; Sparse models; Convex optimization; Symbolic Periodicity; Alternating direction method of multipliers ADMM ; Covariance fitting; multi-pitch estimation problem; Off-grid estimation; Dictionary learning; Atomic norm; Sampling schemes;

    Sammanfattning : 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. LÄS MER